{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "    print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  5.32907052e-17   5.49560397e-17   9.71445147e-18]\n",
      "  std:  [ 0.99999999  1.          1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [ 1.01531428  1.01238373  0.97819988]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "    X = np.random.randn(N, D1)\n",
    "    a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "    batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.70292739451e-09\n",
      "dgamma error:  7.42041421625e-13\n",
      "dbeta error:  2.87950576558e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  0.0\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.07x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.26119551013\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 4.05e-10\n",
      "b1 relative error: 2.22e-07\n",
      "b2 relative error: 4.16e-08\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.99653322011\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 1.85e-08\n",
      "b2 relative error: 7.99e-07\n",
      "b3 relative error: 1.73e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 8.80e-09\n",
      "gamma2 relative error: 5.28e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "    print('Running check with reg = ', reg)\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('Initial loss: ', loss)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "    if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340975\n",
      "(Epoch 0 / 10) train acc: 0.105000; val_acc: 0.111000\n",
      "(Epoch 1 / 10) train acc: 0.300000; val_acc: 0.243000\n",
      "(Epoch 2 / 10) train acc: 0.424000; val_acc: 0.302000\n",
      "(Epoch 3 / 10) train acc: 0.471000; val_acc: 0.302000\n",
      "(Epoch 4 / 10) train acc: 0.576000; val_acc: 0.337000\n",
      "(Epoch 5 / 10) train acc: 0.577000; val_acc: 0.315000\n",
      "(Epoch 6 / 10) train acc: 0.632000; val_acc: 0.343000\n",
      "(Epoch 7 / 10) train acc: 0.684000; val_acc: 0.329000\n",
      "(Epoch 8 / 10) train acc: 0.697000; val_acc: 0.330000\n",
      "(Epoch 9 / 10) train acc: 0.773000; val_acc: 0.326000\n",
      "(Epoch 10 / 10) train acc: 0.792000; val_acc: 0.324000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.123000; val_acc: 0.130000\n",
      "(Epoch 1 / 10) train acc: 0.260000; val_acc: 0.215000\n",
      "(Epoch 2 / 10) train acc: 0.319000; val_acc: 0.273000\n",
      "(Epoch 3 / 10) train acc: 0.336000; val_acc: 0.280000\n",
      "(Epoch 4 / 10) train acc: 0.386000; val_acc: 0.299000\n",
      "(Epoch 5 / 10) train acc: 0.455000; val_acc: 0.325000\n",
      "(Epoch 6 / 10) train acc: 0.486000; val_acc: 0.322000\n",
      "(Epoch 7 / 10) train acc: 0.502000; val_acc: 0.295000\n",
      "(Epoch 8 / 10) train acc: 0.580000; val_acc: 0.314000\n",
      "(Epoch 9 / 10) train acc: 0.600000; val_acc: 0.310000\n",
      "(Epoch 10 / 10) train acc: 0.679000; val_acc: 0.333000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6rqGjRBm/3q443BpBAAi1SYTdmplcY5S6Mg0gdMr4vJpyODWRMBscUXi1b9D2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w68DdMuhhmXNQe4xFM0QbcMO41+xwcF5by+zZtWIK+wAoDxo6NZNif1RCtcQmxkiCsVEREZnBTo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65ba1d13d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "    plt.subplot(3, 1, i)\n",
    "    plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pOvEW2L2myF2rRYTRuGYVndygAopPkzgR6Scia0RknYgctxihiNwpIitFZKmI\nzBCRxs72niKy2OWWKSKDncc+EpGNLo918OVz8IroeDh7JKz5EdZ6seK8Ur6ycyXMfAoSBkLbS/wd\njQpSW/Ye5pN5m7m0c0Pa1A/A+mphETDkEztJ56urILPoBC0xLkpb4lRA8VkSJyKhwFigP5AIXCEi\niQV2WwR0Mca0A8YDzwEYY2YaYzoYYzoA5wCHgZ9djhuZ/7gxZrGvnoNXnXYL1GoBU+/17iLeSnlb\nbjZ8dxNERMEFL2k3qjphz01bTWiIcOe5rfwdSuGi4+GSD2HfBrsKSRGzTxPqR7F572HSMwtZ31ep\nMubLlrhTgXXGmA3GmCzgK2CQ6w5OsnbYuTsfcDdt6RJgqst+wSkswjbd71sP88b6OxqlCvf7C7Bj\nqU3gqvpwcXJVrv29ZT8/LN3ODWc1o15UGRf2LammZ0Kfx2DVZJj7cqG75U9uWLMjvawiU6pIvkzi\n4oGtLve3OdsKMxyY6mb75cCXBbY96XTBviQiEW6OCUwtekObC+C3MZCW7O9olDpeymL7/mw7BBIH\n+jsaFaSMMTz14ypqV4vgprOa+Tscz3QbASddaJdhWz/T7S7/zlDVLlUVIAJiYoOIDAW6AGMKbI8F\n2gLTXDbfD7QBTgFqAvcWcs4bRSRJRJJ2797tk7hPSN8nweTBzw/5OxKljpVzxM5GrVLblsZR6gRN\nW7GDpM37uevcVlSNCJLVHUVg4OtQu7UdH/dCGxgVAy+dDEvHAVA/qjIxVcJ1coMKGL5M4pKBhi73\nGzjbjiEivYEHgYHGmIKDxYYA3xlj/h2AYIzZbqwjwIfYbtvjGGPeMcZ0McZ0qVMngKa112gCZ9wB\nKybYGkVKBYpZT8OulXZx8Mga/o5GBamsnDyembqalnWrcWnnIFuWLaIadLgKsg9B+nbAQNpWmHw7\nLB2HiNiVG7RWnAoQvkziFgAtRaSpiFTCdotOct1BRDoCb2MTuF1uznEFBbpSndY5RESAwcByH8Tu\nW93/BzGNYcpIO4hcKX/busDWy+o4FFqd6+9oVBD74s/NbNprC/uGhQZEZ0/J/PX28duyM+CXxwC7\n/NbqHenk5OaVcWBKHc9nnzBjTA4wAtsVugoYZ4xZISKjRSR/sM0YoBrwjVMu5N8kT0SaYFvyZhc4\n9ecisgxYBtQGnvDVc/CZ8Ejo9wzsXg1/vePvaFRFl3UYJt4MUfHQ92l/R6OCWFpGNq/MWEv3FrXo\n0TqAekDJzJqJAAAgAElEQVRKIm2b++0HtsEH/bg47UNOyVvC5h0BNExHVVg+HaxgjJkCTCmw7RGX\n33sXcewm3EyEMMac48UQ/ad1f2jRB2Y+DSdfAtXr+TsiVVH9+jjsXQfXfA+VA7CWlwoab8xaR2pG\nNg+cl4AEa2ma6Aa2C7WgiOqQm0Wbde/zWaVc8t4bA/EdoXF3aHIGNOyqnx9V5oKwrbucELElR3KP\nwC+P+jsaVVFtmgvz34RT/gPNevg7GhXEtu0/zIdzN3FRxwacFBft73BOXK9HbG+Jq/BIOP9FuOFX\nsu/eyLCc+5gfexVIiC0Z9fkl8GxjeKcHTHsQ1kyFjFS/hK8qliCZNlRO1Wpup7XPeRE6XweNTvN3\nRKoiOXLQzkat0QR6P+bvaFSQmrgomTHT1pCcmgFA2wZB3hrVboj9OWO07VqNbmATO2d7parR7Kxz\nBm+FRXD69afa4Qjb/rJ/EG2ea4fIzHsdEKh/MjQ+A5p0ty12VWoef72l4wq9llLFEVNEderyokuX\nLiYpKcnfYbiXdQheP8V+uG+cDSGh/o5IVRQ/3AFJH8KwqdC4m7+jUUFo4qJk7p+wjIzs3H+3RYaH\n8vRFbRncsaiyoMHt7m+WMGvNbpIecjMiKDsDtiXZhG7THLv+cE6mfaxuotP96iR1G2bZma/ZGUeP\nD4+EAa9qIlfBichCY0yX4vbTljh/q1TV1o775jpI+gBOvcHfEamKYP2v9v3WbYQmcOqEjZm25pgE\nDiAjO5cx09aU6yQuITaK8Qu3sSs9k7rVC6xGER5pV4Boeqa9n3MEkv+GzXNsa93iz2HBu/axkDDI\nyzn2+OwM2zKnSZzygCZxgSBxMDQ9yw4wP+nCirXUkXYllL3MNPh+BNRuBedo0Wl14lJSM0q0vbxI\njLVdxqu2px+fxBUUFmH/UGrcDc5yykqlLLZJ3S+j3B9T2AxZpQrQiQ2BQAT6j7FdqzNG+zuasrN0\nnO1KSNtKwaKayod+esAWMh381vEDuJXy0KIt+wkJcT8DNS6mfL+v8pO4Eyr6GxoODU+xRd+jG7rf\nJzrIiiQrv9EkLlDUbQNdb4a/P4Hkhf6OpmzMGH3sWBA42pWgfGPNT7D4M/sF0qCzv6NRQSgvz/D2\n7PVc+tY8oiqHERF27NdIZHgoI/u29lN0ZSO6SjjxMZGlX36rsJmwvR5xv79SBWgSF0jOvheq1rEr\nOeRVgGrghXUZaFeCbxzeZ1s6651s32tKldDeg0cY9tECnp66mnNPqseskT159uJ2xMdEIkB8TGS5\nn9SQLyE2ipWlTeLaDbGTGFxb5DoO1SElymM6Ji6QVI6Ccx+H726yrSWdrvF3RL5VtTYcclP1vFJV\nyMvVmbreNmUkHN4LV42343SUKoE/1u/h/75aTGpGNo8PPpmhXRshIgzuGF8hkraCEmOr8+vqnWRm\n51I5vBT/V7UbYm95ubbO3Kofodcou46rUsXQlrhA0+4yaNTNDnjN2O/vaHxnz1pbp4wCY2pCwiDr\nIHx9tR0jqLxjxURYPt62wMW283c0Kojk5hlenP4PV733J9UqhzHxlu5cfVrj4F2RwUsS46LIM7Bm\nR7p3ThgSCuc9D+kp8Pvz3jmnKvc0iQs0ItD/OZvAzXzK39H4xuF98MVlTnmVp5yuBLE/B79pn/8/\nU+Gj8yF9p7+jDX4Hd8OPd0JsBzsWTikP7UjL5Ip35/PqjLVc1LEBk0ecQWJckBfz9ZLEWLsqRam7\nVF016grtr4Q/Xoc967x3XlVuaXdqIIptB12Gw4L3bJdq/bb+jsh7crJg3DV2Juq1k+0qFd1uOX6/\nmEYw/np4rxdcOQ7qJZZ9rOWBMfDD/9lWzwvfsjPjlPLAr6t3cte4JRzJyePFIe25qJPOmHTVoEYk\n1SLCSj+5oaA+j8HqH2DqPTD0W/uHvVKF0Ja4QNXzAYisYccxlZdVNYyBKXfBpt9h4OtFLzPWur9d\nSSA3Gz7oa4vTqpJb9o39QjjnQaib4O9oVBDIysnjiR9Wcv1HSdSPjmTybWdoAudGSIiQEFv9xMqM\nFKVaXfv///oZsGaKd8+typ1ikzgR0dHl/lClJvR6FLbMs1/E5cG8sbaEypl3QfvLit8/rgPcMMN2\ns35+qT1Wee5ACky5Gxp2tSszKFWMLXsPc+lbf/DenI1c060x391yOs3r6AD7wiTERrFq+wHy8rz8\nh/YpN9glun667/gyTEq58KQlbq2IjBER7c8qax2vhrhO8PNDkOnlv/bK2pqf7PNIGAg9S7BKQHQD\nuP4naHo2TLoNfnmsYpRfKS1jYNLttvt68Js601cV64elKZz/6u9s2HOIt4Z2YvSgk0s367ICSIyN\n4lBWLlv3H/buiUPD7Njg1C0w52XvnluVK54kce2Bf4D3RGS+iNwoIjqytSyEhMD5z8PBXTD7WX9H\nc+J2LIdvh9uxfhe+ZZ9XSVSOgiu/hs7XwZwX7bmyM30Sarmx6FNYN92Or6nV3N/RqACWmZ3LA98t\nY8QXi2hRrxpTbj+TfifH+jusoJA/ycPrXapg1149+WKY8xLs2+j986tyodhvU2NMujHmXWPM6cC9\nwKPAdhH5WERa+DzCii6+M3S6Gv58C3at9nc0JXdwF3x5OURUhyu+sjNST0RoOFzwMvQZDSsmwCcD\n4dAe78ZaXqRusUtrNTnTdssoVYh1u9IZPHYuX/y5hZvObsa4m7rRsGYVf4cVNFrVq06IeHmGqqs+\nj9uyS9Me9M35VdDzaEyciAwUke+Al4EXgGbAZEBHXZaFXo/a5GfqPcE1ySE7E766yiZbV3wJUXGl\nO58IdP8fXPoxbF8C7/W29ebUUXl58P2tgIFBY0ve6qkqBGMM4xZsZcBrc9mdfoSPhp3C/f0TCA/V\n90tJVA4PpXmdat6foZovOh7OHglrfoS1031zDRXUPBoTBwwCxhhjOhpjXjTG7DTGjAd+8m14CrAr\nG5zzMGycDWOaw6gYeOnkwF4o3hiYNAK2/QUXvQNxHb137pMGw7U/wJF0m8htmuO9cwe7pPdh42/Q\n90mo0djf0agAdPBIDnd8vZh7vl1Kh4YxTPnfmfRoXdffYQWtxLgo33Sn5jvtVqjVAqbeCzlHfHcd\nFZQ8SeLaGWOGG2P+KPiAMeb2og4UkX4iskZE1onIfW4ev1NEVorIUhGZISKNXR7LFZHFzm2Sy/am\nIvKnc86vRaSSB88h+FWqDohdNglj66xNvj1wE7nfnrezas95GBIHev/8DU+B//xip+N/MhiWfO39\nawSbveth+iPQojd0utbf0agAtDw5jQGvzWHSkhTu7NOKz/7TlXpRlf0dVlBLiI0iJS2T1MNZvrlA\nWCU7yWHfepj3um+uoYKWJ0ncWBGJyb8jIjVE5IPiDnJKk4wF+gOJwBVuZrguAroYY9oB44HnXB7L\nMMZ0cG6uWcCzwEvGmBbAfmC4B88h+M18AijQlZqdATNG+yWcIi2fYONtd7ktJ+IrNZvC8J9tvbnv\nboRZzwRXd7M3LB1nW2VHxcAbp9nnP/A1LRCqjmGM4aO5G7nojT84nJXDlzecxu29WhIaou+T0kqM\ndSY3+KpLFaBFL2hzgf3jOG2b766jgo6nLXGp+XeMMfsBT/rGTgXWGWM2GGOygK+w3bL/MsbMNMbk\nz82eDxRZUVLsYn3nYBM+gI+BwR7EEvwK++AG2gc6eSFM/K+tTTbwVd8nE5E1YOgEaH8FzHraXjvH\nR38RB5ql42xrbNpWwEBuFphc7V5WTFyUTPdnfqXpfT/S7ekZDHhtDqMmr+SMlrWZ+r+z6Nqslr9D\nLDcSYn04Q9VV36fA5NlSTUo5PEniQkSkRv4dEamJZ8t1xQNbXe5vc7YVZjgw1eV+ZRFJcsqa5Cdq\ntYBUY0xOced0SqEkiUjS7t27PQg3wEUXkt+GR9p1VgNBWjJ8eaXt4rzscwiLKJvrhlWytdB6PABL\nvoTPLgqc18SXZow+vhBoblZgts6qMjNxUTL3T1hGcmoGBtielsnylAMM7hDH+9d2oWbVijECpazU\nqR5BneoRrNqe7tsL1WgMZ9wJK76DDbN9ey0VNDxJ4l4A5onI4yLyBPAHx3Z7lpqIDAW6AGNcNjc2\nxnQBrgReFpESFbsyxrxjjOlijOlSp04dL0brJ70esQmbq5Aw+yU+tius/tE/ceXLOmRLiWQdgiu+\nhmpl/JqLQI974cJ3YOuf8F6f8l9bKVhaZ1WZGjNtDRnZucdtX7BpP6Ld7D6RGBvl2+7UfN1vh5jG\ntlJBbrbvr6cCnid14j4BLgZ2AjuAi4wxn3pw7mSgocv9Bs62Y4hIb+BBYKAx5t+pN8aYZOfnBmAW\ntgt3LxAjIvktgW7PWS61GwIDXrVLUCH25+A34abZULUufHWlXTDeH7XT8vJgwo2wczlc8oF/F6tv\nfxlcPREO7bYzV7cu8F8svpKZ5tSNKmT8X2GttqpCSEl1v0xTYdtV6SXERrFuVzpZOT5eTSY8Evo/\nC7tXw1/v+PZaKih4VBTIGLMCGAdMAg6KSCMPDlsAtHRmk1YCLneO/5eIdATexiZwu1y21xCRCOf3\n2kB3YKUxxgAzgUucXa8FvvfkOZQL7YbAHcthVKr92W4IxLaHG2fapaxWToKxp8Lyb8t2gP+vo+0i\n632fglbnlt11C9Oku525GlEdPr7Adj+UB3m5kPQhvNrJrkPbuDuEFZhZGB5pW21VhbR6xwFCCpms\nEBcT6Xa7Kr3EuCiycw3rdh30/cVa9YOW58LMpyF9h++vpwKaJ8V+B4rIWmAjMBvYxLFj19xyxq2N\nAKYBq4BxxpgVIjJaRPJnm44BqgHfFCglkgAkicgSbNL2jDFmpfPYvcCdIrIOO0bufc+eajkWGm4L\nQt70m21qH389fD20bD7gi7+wy8J0HgZdb/b99TxVuyX8Z4ZNcr+5zq4/GMwzVzf+Dm+fDT/8n31u\nN86EYVPsTFTX1tkBr9rkXlU4ExclM3jsXKqEh1Ap7Nj/2iPDQxnZt7WfIiv/ymSGaj4R6PcM5B6B\n6Y/6/noqoIkp5ovNSaTOAX4xxnQUkZ7AUGNM0JT26NKli0lKSvJ3GGUjNwfmvwEzn7StNP2egfaX\n+2aW6OZ58PEAaHw6DP3WJpOBJjvTzlhdMcEuQ7V/o52AEd3AtlgFesKzfxP8/DCsmmSTtD6PwUkX\naQkR9a+snDye/HElH8/bzKlNavL6lR35Y/1exkxbQ0pqBnExkYzs25rBHYuaV6ZKIzfPcNKjP3FV\n18Y8fEEZDSeZMRp+fwGG/QSNu5XNNVWZEZGFzryAovfzIIlLMsZ0cZK5jsaYPBFZYoxp761gfa1C\nJXH59qyzyy9tnQ8t+sCAl707VmrfRnivly3x8Z9f7M9AlZcHX18FawqsEhceGbgtV0fS4fcXbbdp\nSCiccQecftvxk1tUhbYjLZNbPl/I31tS+c8ZTbm3fxtdOstPBo2dS5XwUL688bSyuWDWIXj9FIis\nacdGh4SWzXVVmfA0ifPk054qItWA34DPReQV4FBpA1Q+VrsFDJtqK31vngtjT7PjqbzRpZiZBl9c\nZsdoXTkusBM4sOuH7lh2/PbsDJjxWNnHU5S8PFj0ObzWGea8aJcYG5EEZ9+jCZw6xrz1e7ngtd9Z\nvSOd16/syEMXJGoC50eJsdVZuf0AxTWMeE2lqnZ5vZ3LIKnY+vuqnPLkEz8IOAzcgV0rdT0wwJdB\nKS8JCYGuN8F//4D4jnY81ScDS1d6IzcHvhlml4C57FOoVaLKL/5TVDmOCTfaEi0Fa66VtS3z4b1z\n4PtbbNfp8F/surPR2g2mjjLG8Pbs9Qx9/0+iI8P5/tbuXNAuzt9hVXiJsVGkZWSzPS2zDC86GJqe\nDb8+7p/KBMrvikzinKWzfjDG5BljcowxHxtjXjXG7C2j+JQ31GwK10yCAa9A8iJ483SY/5Zt9Smp\naQ/A+hlw/gvQ9Czvx+orhRZLrgprf7YlWsa0sJNCVn4PWYfd7+8LqVvtdT/oayejXPgODJ9u14dV\nykV6Zjb//exvnp66mr4n1eP7EWfQsl51f4elsDNUoQxWbnAlYntbsg4FXq+CKhNFJnHGmFwgT0Si\nyyge5Ssi0Pk6uHW+LU3x073wYX/Ys9bzcyx4D/56G0671Z4rmLgrlhweaccK3r0Wrv4O2l4CG2bB\nuGtgTHP7c/m3cMRHZQOyDsHMp+y4ltU/wln3wG0Lba27EO0WU8dauzOdQWPnMn3VTh48L4GxV3ai\nWoQni+eostC6vk3iVpXFDFVXddvYygB/fwrbFpbttZXfeTKx4Xtsod3puIyFM8bc7tvQvKdCTmwo\nijGw9GuYeq/tQuz5AHQbAaFFfCGs/xU+uwRa9IYrvgzOQbRLx9kZXWnbCp+dmpsDW/6wrXErJ8Gh\nXXaWb4vekDgIWvWFyqX8m8YYWPYN/DIKDiTb2aZ9HoMYT8ovqopo8pIU7v12KVUqhfL6lZ04Tdc+\nDUg9xswkITaKN4d2LtsLZx6A17tAVLwtraR/BAY9b85OvdbddmPMxycYW5nTJK4Q6Tvhxzttod64\njjDoDferLez+x65+EN0Ahk+zRXQrgrxcu4RXfkKXngKhlaD5OTaha92/5JM6khfC1Ptg21+2hl2/\nZ2yJFqXcyM7N4+kpq/lg7kY6N67B2Cs7UT+6cvEHKr/472cLWbn9ALNH9iz7iy/5Gr670daO7HRN\n2V9feZXXkrjyQJO4IhhjVzSYMtLOOj37HlvOYsV3R1utQkIgrArc8kfFbS3Ky4PkJCeh+x7Sttq1\na5v1cBK686GqS+tIwVa/02+HlL9hyZd2mbRej0CHq/QvZlWoXQcyufWLv1mwaT/Xnd6EB85LOK6I\nrwosr81YywvT/2H5Y33LvqvbGGeIzD92WEagVw1QRfJmS9xG3CzSaIxpduLhlS1N4jxwaI/tXl0+\nHqIawOHdkHPk6OOhETDo9cCsqVbWjLEJ2YqJNqFL3QwSCk3PtAldnoHpDx4/21VCba23M++CylH+\niV0FhQWb9nHL539zMDOHZy5uy6AOOkM5GMxYtZPhHycx/uZudGlSs+wD2LEM3j4LTvkPnDem7K+v\nvMbTJM6TPxVcT1IZuBTww7tT+VTV2nDJ+3DyRXbJLlNg5mruEduypEmcnSQS39ne+oyG7UucFrqJ\n8MMdhR9Xra4d+6ZUIYwxfDB3E09PWUXDmlX4bHhXWtevIMMXyoEEl+W3/JLE1W9rE7gF70HHqyG2\nXdnHoMpUsW3zxpi9LrdkY8zLwPllEJvyhzbnF14QuLBaaxWZCMR1gN6Pwm1/w81zC99XF6tWRTh0\nJIfbvlzE4z+spGebunw/orsmcEEmNroyMVXCy36GqqueD9iu1Ckjg3u9aOWRYlviRKSTy90QbMuc\nzmsvz6Ib2DFf7rarwolA/ZNtoV59/VQJrN99kJs/Xcj63Qe5p19rbj6rOSEhuj5usBEREmOjyrZW\nXEGRNaD3KJh0mx2b2/4y/8WifM6TUbIvuNyeBjoB2qdWnhVWU63XI/6JJ9jo66dK4Kfl2xn0+lz2\nHsri0+FduaVHC03gglhCbBSrd6STk3sCxdS9pcNQiOsE0x+25UdUuVVsi5oxxg9zpZVf5Y97K66m\nmnJPXz/lgZzcPMZMW8Pbv22gfcMY3ryqE3Exuj5usEuMjeJITh6b9h6iRV0/dYeHhMD5z8O7vWD2\ns3aNVVUuedKd+hTwnDEm1blfA7jLGPOQr4NTftRuiCYdpaGvnypg4qJkxkxbQ0pqBvWiKlM1IpT1\nuw8x9LRGPHxBIhFhQVhAWx0nf3LDipQD/kviwE686nQN/PmWneRQt43/YlE+40l3av/8BA7AGLMf\nOM93ISmlVPkycVEy909YRnJqBgbYcSCT9bsPceWpDXlicFtN4MqRFnWrER4qrNqe7u9QoNejUKka\nTNVJDuWVJ0lcqIhE5N8RkUggooj9lVJKuRgzbQ0Z2bnHbZ/9zx4/RKN8qVJYCC3rVmelP2eo5qta\nC855CDb+ZksgqXLHkyTuc2CGiAwXkeHYNVSDZsktpZTyt5TUjBJtV8Etwd8zVF11ud7Wj5v2IGQd\nKn5/FVQ8qRP3LPAEkODcHjfGPOfJyUWkn4isEZF1InKfm8fvFJGVIrJURGaISGNnewcRmSciK5zH\nLnM55iMR2Sgii51bB0+frFJK+UNsjPv1TnUiQ/mUGBfFnoNH2JWe6e9QICQUznseDiTDC61hVAy8\ndLItP6KCXrFJnIg0BWYZY+42xtwN/CYiTTw4LhQYC/QHEoErRKTg6uqLgC7GmHbAeCA/OTwMXGOM\nOQnoB7wsIjEux400xnRwbouLi0UppfzptGa1jtsWGR7KyL6t/RCN8rVEZ3JDQIyLA0jdYpf9O5IO\nGFvHcvLtmsiVA550p34DuBa8yXW2FedUYJ0xZoMxJgv4ChjkuoMxZqYx5rBzdz7QwNn+jzFmrfN7\nCrALqOPBNZVSKqBs2nOIqct20KZ+deJjKiNAfEwkT1/UlsEddU3U8uhoEhcgXaozRoMpMCYzO8Nu\nV0HNk5UXwpwkDABjTJaIVPLguHjAtWz9NqBrEfsPB6YW3CgipwKVgPUum58UkUeAGcB9xpgjbo67\nEbgRoFGjRh6Eq5RS3pWbZ7j7myWEhwofDTuV+tHuu1VV+RJdJZz4mMjAGRdX2JKJupRi0POkJW63\niAzMvyMigwCvTqkSkaHY5bzGFNgeC3wKDDPm3xXZ7wfaAKcANYF73Z3TGPOOMaaLMaZLnTraiKeU\nKnvvz9lA0ub9jBp4kiZwFUxCbIDMUIXCl/yTEFjwHuQc1w6igoQnSdzNwAMiskVEtmKTpps8OC4Z\naOhyv4Gz7Rgi0ht4EBjo2qImIlHAj8CDxpj5+duNMduNdQT4ENttq5RSAWXtznSe//kf+iTW40Lt\nNq1wEmOj2LD7IJluSsuUOXdLAYZGQI0m8ONd8EoH+PNt28Wqgoons1PXG2NOw05OSDDGnG6MWefB\nuRcALUWkqdP9ejkwyXUHEekIvI1N4Ha5bK8EfAd8YowZX+CYWOenAIOB5R7EopRSZSYnN4+7vllC\n1UqhPHVhW+x/V6oiSYyLIs/Amh0BMLmh3RAY8CpENwTE/hz0Oty2EK753iZzU++BV9rDvLGQdbi4\nM6oA4cmYOETkfOAkoHL+f0bGmCJHRBpjckRkBDANCAU+MMasEJHRQJIxZhK2+7Qa8I1z3i3GmIHA\nEOAsoJaIXOec8jpnJurnIlIHEGAxtqVQKaUCxpuz1rN0Wxpjr+xEnepaG70iyl9+a+X2A7RvGFPM\n3mWgsKUAm/Wwt01z7Dqr0x6AOS/B6bdBl+EQUa1s41Ql4snaqW8BVYCewHvAJcBfnpzcGDMFmFJg\n2yMuv/cu5LjPgM8KeewcT66tlFL+sDLlAK/+upYL2sVyfrtYf4ej/KRhjSpUiwgLnBmqxWlyhr1t\nnge/PQfTH4E5L8PpI+CUG6BylL8jVG54MibudGPMNcB+Y8xjQDeglW/DUkqp4JOVk8ed4xYTHVmJ\nxwed7O9wlB+FhIid3BAoM1Q91bgbXP0dDP8FGnSxZUhebguzn4OM1OKPV2XKkyQuf6TjYRGJA7IB\n/fNSKaUKeO3Xtazekc7TF7WlRlVPKjGp8iwhNorVO9LJywvCxecbngJXfQM3zITGp8PMJ+HldjDz\nKcjY7+/olMOTJO4HZ7WEMcDfwCbgC18GpZRSwWbJ1lTemLWeizs1oE9iPX+HowJAYmwUB4/ksHV/\nEE8UiO8EV3wJN/0Gzc6y4+ZeagszHofD+/wdXYXnyezUx40xqcaYb4HGQBvXcW1KKVXRZWbnctc3\nS6hTLYJHBhRcXVBVVP9Obgi2LlV3YtvDZZ/BzXOhRS/4/QW7Buv0R+Hgbn9HV2F50hL3L2PMEWNM\nmq+CUUqpYPTi9H9Yt+sgz13SjujIcH+HowJE6/rVCZEAWn7LG+qfDEM+hlvmQev+MPcVeKUdTHsQ\n0nf6O7oKx6MSI0oppdxbsGkf7/6+gSu7NuKsVro6jDqqcngozetUC5yVG7ypbgJc8j6cfa9tlZv/\nhl39ofMwqNkM/njVLusV3cAWG3ZX3kSVmiZxSil1gg5n5XD3N0uIj4nkgfMS/B2OCkAJsVEkbSrH\nY8fqtIKL3oaz74HfX4Q/3wJcJnKkbYXJt9vfNZHzukK7U0WkU1G3sgxSKaUC0bNTV7N572HGXNKe\nahH6N7E6XmJcFClpmaQezvJ3KL5VqzkMHgvV6x//WHaGLVWivK6o/3VeKOIxA2jRXaVUhfXHuj18\nPG8zw7o3oVvzWv4ORwWoRJeVG05vXtvP0ZSB9B3ut6dtK9s4KohCkzhjTM+yDEQppYJFemY2I8cv\npWntqtzTt42/w1EBLH+G6qrt6RUjiYtuYLtQ3W1XXufp2qknA4lA5fxtxphPfBWUUkoFsid/XMX2\ntAy+ufl0IiuF+jscFcDqVI+gTvWI8lFmxBO9HrFj4LIzjm4Li7Dbldd5snbqo0APbBI3BegPzAE0\niVNKVTgz1+ziqwVbufns5nRuXMPf4aggkBAbVT5nqLqTP3lhxuijXaj1O+ikBh/xpE7cJUAvYIcx\nZhjQHoj2aVRKKRWA0g5nc9+3S2lVrxp39Gnp73BUkEiMjWLdrnSycvL8HUrZaDcE7lgOo1Kh682Q\nshAOpPg7qnLJo7VTjTF5QI6IRAG7gIa+DUsppQLPqMkr2HMwixcu7UBEmHajKs8kxkWRnWtYt+ug\nv0Mpe11vApMHf73r70jKJU+SuCRn7dR3gYXY9VPn+TQqpZQKMNNW7OC7Rcnc2rMFbRtoZ4TyXGJs\ndYCK06XqqmZTaHM+JH0AWYf8HU2548naqbc4a6e+BfQBrnW6VZVSqkLYe/AID363jJPiohjRs4W/\nw1FBpmntalQODylfy2+VRLcRkJkKS770dyTlTrFJnIhMEpErRaSqMWaTMWZpWQSmlFKBwBjDw98v\nJ8wlZZgAACAASURBVC0jmxeGtKdSWImWnFaK0BChdb3qFWeGakENu0JcJ5j3BuRVkHGBZcST/41e\nAM4AVorIeBG5REQqF3eQUkqVB5OXbmfKsh38X+9WtKkf5e9wVJBKjIti1Y4DGGOK37m8EYFut8K+\n9bD2Z39HU6540p062xhzC9AMeBsYgp3cUCwR6Scia0RknYjc5+bxO0VkpYgsFZEZItLY5bFrRWSt\nc7vWZXtnEVnmnPNVERFPYlFKqZLadSCTR75fToeGMdx0VjN/h6OCWGJsFKmHs9melunvUPwjcRBE\nxcO81/0dSbniUb+AiEQCFwM3A6cAH3twTCgwFltXLhG4QkQSC+y2COhijGkHjAeec46tCTwKdAVO\nBR4VkfyCTG8CNwAtnVs/T56DUkqVhDGG+ycsIyMrlxeGtCcsVLtR1YnLX7mhwnaphobbmaqbfoft\nOirLWzwZEzcOWIVdK/V1oLkx5jYPzn0qsM4Ys8EYkwV8BQxy3cEYM9MYc9i5Ox/IX5ejLzDdGLPP\nGLMfmA70E5FYIMoYM9/YNulPgMEexKKUUiUyfuE2Zqzexci+rWlep5q/w1FBrs2/y29V0CQOoNO1\nEF4V5r/h70jKDU+W3XofuMIYk1vCc8cDrguobcO2rBVmODC1iGPjnds2N9uVUuXIxEXJjJm2hpTU\nDOJiIhnZtzWDO5bdRz0lNYPRk1dyapOaXN+9aZldV5Vf1SLCaFKrSsUsM5IvMgY6XgVJH0LvUVC9\nvr8jCnqejImbdgIJXImIyFCgCzDGi+e8UUSSRCRp9+7d3jqtUsrHJi5K5v4Jy0hOzcAAyakZ3D9h\nGRMXJZfJ9Y0x3PvtUnKNYcyl7QgJ0WG3yjsq1PJbhel68/+zd9/hUZXZA8e/Jz2EQCD0BOkGIr1J\nExFR0FVEQOx1V1dddSuoW9TV9Scr7rpr2WJbXTtNxLaAAiKKSugSiHRIgiGUQIAEUs7vj3sDQ0gn\nk5nJnM/zzJOZW957bsnkzVuhuBCWv+TrSOoFbzbyyODUmR0S3WWnEJFRwO+Asap6rJJ9MzhZ5Vpu\nmgCq+oKq9lfV/s2bN6/xSRhj6ta0eWnkFZz6f2NeQRF//t/GOjn+m9/s5ItNe3nw0m60i4+pk2Oa\n4JDcuhE79h3l8LFCX4fiO/GdIOlSWP4yFOT5OpqA581M3HKgi4h0EJEI4BpgrucGItIHp8frWFX1\n7PE6D7hYRJq4HRouBuap6m7gkIgMcnul3gS878VzMMbUscycsr/Ydx/M5+KnP+c3M9bw32XbWb0r\nh/yC2q0k2LnvKP/38QaGdW7GDeeeVatpG1PSuWFjsJfGDf4Z5O2HNe/4OpKAV2mbOBH5TFUvrGxZ\naapaKCL34GTIQoFXVHW9iDwKpKjqXJzq04bADHekkJ2qOlZV94vIYzgZQYBHVXW/+/5u4FUgGqcN\n3ScYY+qNNnHRZJSRkYuNCiMhLppFG/cwc4XTNDY8VEhqFUuPhDh6JTamR2Jjzm4ZS3gNepIWFyu/\nmbmGUBH+PLEnNnqRqW3JbU52bujfvqmPo/GhdkOgdS+ng0PfmyHEen7XVLmZOHdA3wZAM7c0rOQb\nrRFV7Eygqh8DH5da9pDH+1EV7PsK8EoZy1OA7lU5vjEm8EwencQvp6/Gc0zU6PBQHruiO+P6JKCq\nZB7MZ116DmvSD7Iu/SAfrc3k7W93AhAZFkJym0b0SoyjR0JjerVtTIdmDQkto22bZweKRtFhHMwr\n5MmJPUmIi66r0zVBpHXjKOIahFu7OBFnKq7Zt8OWz6DLRb6OKGBVVBL3U+AXQBucie9LvgEP4Qw1\nYowxtW5Qx3hUoVFUGLn5haf1ThUREuKiSYiLZkz31oDTGWHHvqOsSc9hrZuxm56yi1e/2g5ATEQo\n3RMa0zOxMT0T4+iZ2JiVOw7w2/e+O9H+7mBeISEC4daRwXiJiNCtVaPgHSvOU/I4WPCQM/ivZeJq\nrNxMnKr+Hfi7iNyrqs/WYUzGmCC2YEMWALPuGkKXlrFV2kdEaN8shvbNYriit5PZKypWtmQfZs2u\nHNZlHGRN+kFe+2oHx4u2uftA6RmQihWemv89V/ZNLH0IY2pFcptGvPH1DgqLioN7AOmwCBh4O3z2\nKGSth5bn+DqigFSVceJ+EJFYVc0Vkd8DfYE/qepKL8dmjAlCC1KzaB/fgM4tzmyA3dAQ4eyWsZzd\nMpar+jud3Y8XFvN9Vi5r0w/y2/fWlblfeR0rjKkNya0bcaywmO37jtC5RdX+Sam3+t0KS55y2sZd\n8byvowlIVfk34A9uBm4YMApn8N9/ejcsY0wwys0vYNmWvVyU3NIrHQsiwkLontCY6849q9x2b22s\nPZzxohPTb+3O9XEkfqBBU+h1LaydDoerNCW7KaUqmbiSPvw/Al5Q1Y+ACO+FZIwJVp9/n01BkXJR\nsvdHcp88Oono8NBTlkWHhzJ5dJLXj22CV+cWDQkPFWsXV2LQ3VB03Bk3zlRbVTJxGSLyb+Bq4GMR\niazifsYYUy0LUrNoGhNBv3ZNvH6scX0SeGJ8DxLiohEgIS6aJ8b3qNPpvUzwiQgLoXOLWOuhWqJZ\nZzh7jDODQ0G+r6MJOFVpEzcJGAM8pao57iT0k70bljEm2BQUFbNo4x4uPqdVmcOBeMO4PgmWaTN1\nLrl1I5ZssukgTxj8M3jtclg3Hfre5OtoAkpV5k49CuwBhrmLCoFN3gzKGBN8vt22n0P5hVyU3NLX\noRjjVcltGpGde4w9uVbyBED786BlD1j2j9O7jJsKVZqJE5GHgfuBB91F4cAb3gzKGFM1c1ZlMHTq\nQjo88BFDpy6ss0nivWFBahaRYSGc16WZr0Mxxqu6tXZ6pW6wzg0OERh8N2RvgC0LfR1NQKlK27Yr\ngbHAEQBVzQSCvF+0Mb43Z1UGD85eR0ZOHgpk5OTx4Ox1AZmRU1UWpGYxrHMzGkRUpZWHMYErufXJ\n6beMq/sEaNjSGW7EVFlVMnHHVVUBBRCRGO+GZEzg80YJmapyMK+AbXuPsGLHfh79MPXEbAMl8gqK\nmDYv7YyPVddSdx8iIyfPqlJNUIhrEEFCXLT1UPUUFgkDbofNn8Kejb6OJmBU5V/e6W7v1DgRuR24\nDXjRu2EZE7hKSshKMlglJWTAKY3oi4uVnLwC9h85xr7Dx9l/5Dj7jjg/T74/ue7A0eMUFFXeXiQQ\nB6tdkJqFCFzYzTJxJjh0ax1rJXGl9b8NvnAH/x37jK+jCQiVZuJU9SkRuQhnztQk4CFVXeD1yIwJ\nUNPmpZVZQvbA7LW8/e3OE5m0A0ePU1xOniw2MoymDSNoGhNBYpNoeiY2pmlMJPExzrKmDSOYMnMt\n2bnHTts3EAerXZCaRZ+2cTSPjfR1KMbUieTWjVi4cQ/5BUVElRqvMGjFxEOva2D123DhQxBj7WMr\nU6XGJ26mbYGINAP2eTckYwJbeSVh+QXFFKvSsXkM/ds3PZEhi3cza01jIoiPiaRJTDiRYZV/qf/u\n0m6nlPgBRIWHBNxgtRk5eazPPMT9Y7r6OhRj6ky31o0oVkj7IZdebeN8HY7/GHQ3rHgVUl6B86f4\nOhq/V24mTkQGAVOB/cBjwOtAMyBERG5S1f/VTYjGBJY2cdFklJGRS4iLZsadQ2rtOCVVs9PmpZHp\ndm64olebgBv37NNUZ8J7aw9ngklym5OdGywT56F5EnS+CL59EYb+3GkrZ8pVUceG54D/A94GFgI/\nUdVWwHDgiTqIzZiA9KuLupy2zFvTOY3rk8CXD4xk6xOX0ql5DFv3Hqn1Y3jbgtQsOjaLOeMJ740J\nJG2bNKBhZJjN3FCWwT+DI3tg3UxfR+L3KsrEhanqfFWdAfygql8DqKp1GzGmAo2inamFm8ZE1Nl0\nTiLChH6JLN9+gB37Aicjdyi/gK+37rNSOBN0QkKErq1irYdqWTqOgBbnOB0cbPDfClWUiSv2eF+6\nbsiuqjHlmJGyi2YNI/jmtxeybeqP+PKBkXVSxXllnwREYPbKwBknbnFaNoXFapk4E5SS2zRi4w+5\nFJfXwylYicCguyDrO9j2ua+j8WsVZeJ6icghEckFerrvSz73qKP4jAkoew8fY+HGPYzvm0h4aFWG\nYaw9rRtHM7RTM2avSkcD5L/XBalZxMdE0Ocs7094b4y/6da6EYePFbLrwFFfh+J/elwFMc2dqbhM\nucr9K6OqoaraSFVjVTXMfV/yObwqiYvIGBFJE5HNIvJAGeuHi8hKESkUkYkeyy8QkdUer3wRGeeu\ne1VEtnms612TEzfGG+asyqCwWLmqX6JPjj++bwK79uexfPsBnxy/Oo4XFrN44x4u7Naizia8N8af\n2MwNFQiPggE/gU3zIPt7X0fjt7xWVCAiocDzwCVAMnCtiCSX2mwncAvwludCVV2kqr1VtTcwEjgK\nzPfYZHLJelVd7a1zMKY6VJXpKbvo3TaOLi19MzPdmO6taBARyuyV6T45fnV8s20fuccKuSi5la9D\nMcYnklrFEiJYu7jy9P8xhEbCN//0dSR+y5v1PQOBzaq6VVWPA+8AV3huoKrbVXUtp7a/K20i8Imq\nWnmz8Wtr0w/yfdZhrurvm1I4gAYRYVzSvTUfrd1NfqkBh/3NgtQsosJDGNbZBvQ0wSkqPJSOzRta\nD9XyNGwOPSc5g/8e3e/raPySNzNxCcAuj8/p7rLqugZnmBNPj4vIWhF5WkTKHERGRO4QkRQRScnO\nzq7BYY2pnhkrdhEZFsLlvdr4NI4J/RLIPVbIfHf8NX+kqnyamsV5XZoTHWGj1Zvg1SgyjEUbs2t1\nnuV6ZfDPoDDPGfzXnKZuW15Xk4i0xulEMc9j8YNAV2AA0BS4v6x9VfUFVe2vqv2bN2/u9VhNcMsv\nKOL91Zlc0r0VjaKq1GTUawZ1iCchLppZK/y3SnV95iEyD+Zbr1QT1OasymBtxkGKVFFOzrNsGTkP\nLbpBp5HO4L+Fx30djd/xZiYuA2jr8TnRXVYdk4D3VLWgZIGq7lbHMeA/ONW2xvjUvPU/kJtfyKT+\nbSvf2MtCQoQr+yTwxaZs9hzK93U4ZZqfmkWIwIVdW/g6FGN8Ztq8NApLDS+SV1DEtHlpPorITw36\nGRz+AdbP9nUkfsebmbjlQBcR6SAiETjVonOrmca1lKpKdUvnEBEBxgHf1UKsxpyRGSnpJDaJZlDH\neF+HAji9VIsV5qz2z//oF6Rm0a9dE+Ib2pQ6JniVN89yecuDVucLoXlXWPa8Df5bitcycapaCNyD\nUxW6AZiuqutF5FERGQsgIgNEJB24Cvi3iKwv2V9E2uOU5JUe6e9NEVkHrMOZy/VP3joHY6oi/cBR\nvtyyl4n9Egnxk6EyOjZvSJ+z4pi1IsPvxozbtf8oG3YfsqpUE/TaxEWXuTyuQbgNAOypZPDfH9bC\n9qW+jsaveLVNnKp+rKpnq2onVX3cXfaQqs513y9X1URVjVHVeFU9x2Pf7aqaoKrFpdIcqao9VLW7\nqt6gqoe9eQ7GVMbJKMGEvr7rlVqWCX0TScvKZb2fDV/w6YaSCe9taBET3CaPTiI6/NSOPQIcOFrA\nj55dyqK0PX73T5jP9LwaGsQ7U3GZE/y6Y4Mx/q64WJm5chdDO8fTtmkDX4dzist6tiYiNIRZfjZm\n3ILULDq3aEiHZjG+DsUYnxrXJ4EnxvcgIS76xDzLf72qF3+/pjdHjhVy63+Wc80LX7Nyp/8P3u11\n4dHOuHFpn8C+Lb6Oxm+E+ToAYwLZ19v2sWt/Hr++KMnXoZwmrkEEo5JbMHd1Jr+9tFudTwNWloNH\nC/hm237uGN7R16EY4xfG9Ukoc27lS7q35p3lO3nms02M/8dXXJzckiljkujcwjcDifuFAT+BL/8G\nX/8TfvSUr6PxC77/VjcmgM1MSSc2Kowx3f2zanB8n0T2HTnO52n+MVbiorQ9FNmE98ZUKiIshJsG\nt+fzyRfwq4vO5qst+7j46SVMnrEmeDs+xLZ05lRd/SbkWekkWCbOmBo7lF/Ax9/t5vJebYgK988B\na89Pak58TASzV/lHleqC1Cyax0bSOzHO16EYExBiIsO478IuLJlyAbcO7cD7qzMZ8dRiHv8olQNH\ngnDctEF3Q8FRWPGqryPxC5aJM6aGnKmtiv1ibLjyhIeGMLZ3Gz5N3UPOUd9+4R8rLGJx2h5GdWvh\nN714jQkUTWMi+MNlySyaPIKxvdrw8tJtDH9yEc8t3MTR44W+Dq/utOoOHc6Hb16AooLKt6/nLBNn\nTA1NT9lFlxYN6ZXY2NehVGhC30SOFxXz4drdPo1j2ZZ9HDleZFWpxpyBhLhonrqqF//7xXAGdYrn\nqfnfM/zJxby+bDvHCyuahrweGfwzyM2E9XN8HYnPWSbOmBrYvCeXVTtzmNS/Lc640/7rnDaNSGoZ\n6/NeqgtSs2gQEcqQTjbhvTFn6uyWsbx4U39m3TWYjs1i+MP76xn11895f3VG/R9jrvNFEN8FvrbB\nfy0TZ0wNzEhJJzREyuxV5m9EhAn9Eli1M4et2b4ZVrG4WPl0QxbDuzT32/aDxgSifu2a8u5PB/Gf\nWwbQICKUn7+zmsueXcri+jzGXEiIM/hv5irYuczX0fiUZeKMqaaComJmrczggqQWNI8NjGmjxvVO\nIERg9krfTMO1LuMgWYeOWVWqMV4gIlzQtQUf33cef7u6N7nHCrjlP8u59sV6PMZcr2shuokzFVcQ\ns3HijKmmz9Oy2Xv4GJP6+9cMDRVp0SiK87o0571VGfzqorPrvGPBAnfC+wtswntjvCbErR24tEdr\n3v52J88udMaYG31OS/qeFcd/l+0kMyePNnHRTB6dFBA1CeWKaAD9b4Mv/gr7t0LT4Bx70krijKmm\nGSt20axhRMBlSMb3TSAjJ4+vt+2r82MvSM2if/umNI2JqPNjGxNsIsJCuHnIyTHmFm/cwxOfpJGR\nk4cCGTl5PDh7HXNW+aZkvtYMuB0Q+Nd58EgcPN0d1k73dVR1yjJxxlTD3sPH+GzDHq7sk+AXMyBU\nx+hzWhEbGVbnVao79x0lLSuXi60q1Zg6VTLGXJOGpzf7yCsoYtq8NB9EVYu2fwEicPwwoHBwF3xw\nX1Bl5ALrr5AxPjZnVQaFxcpVfjw2XHmiwkO5tEdrPlm3u07HlZqf+gOAtYczxkeyDuaXuTzgZ374\n7FHQolOXFeTBJ/fD7rXO+3rO2sQZU0WqyoyUdHq1jePsloE5f+GEfom8m7KLeet/4Mo+ddOmb0Fq\nFme3bEi7eJvw3hhfaBMXTUYZGbY2cdE+iKYWHSxn2KS8/fDv8wCBJu2heVdonnTyZ7OzIbJhXUbq\nNZaJM6aK1mUcJC0rlz+N6+7rUGqsf7smtG0azawVGXWSiTtw5DgpOw5w5/nB2ejYGH8weXQSD85e\nR17BqaVW4/sGcMcGgMaJThVqaQ1bwSVTITsNsjc6Pzd/CsUeMzw0PsvN2JVk7rpC87MhqpzB29dO\nd0r+DqY7x73wIeg5yTvnVQ2WiTOmimakpBMZFsLlvdr4OpQaCwkRxvdJ5JmFm9h9MI/Wjb37n/jJ\nCe9befU4xpjylfRCnTYvjcycPFo1juJYYRGzV2Zwx/COxEaF+zjCGrrwIacNnGe1aXg0XPwYnHPl\nqdsWFcKBbW6mbuPJDN72L6DQo7o5ts2ppXbNu0L2Bpj325PHKWl7Bz7PyFkmzpgqyC8o4v3VGYzp\n3orG0QH6heca3zeBv3+2ifdWZXD3iM5ePdaC1CxaxEbSM8G/pyYzpr4b1yfhlCFFVuzYz1X/WsYf\nP0jlqat6+TCyM1CSgapKCVloGDTr4ry6XX5yeXER5Ow4tdQueyOsfA0KjpZ/7II857iWiTPG/81P\nzeJQfqFfT3ZfVe3iYxjQvgmzVqRz1/mdvDZtWH5BEZ9/n824Pgk24b0xfqZfu6bcPaIzzy3azKhu\nLRjTvbWvQ6qZnpPOLCMVEuqMMde0IyRdcnJ5cTEcSncydW9OLHvf8trk1SGv9k4VkTEikiYim0Xk\ngTLWDxeRlSJSKCITS60rEpHV7muux/IOIvKNm+a7ImIDTxmvm5Gyi4S4aAZ3jPd1KLVifN9EtmQf\nYW36Qa8dY9mWfRy1Ce+N8Vs/H9WFHgmNeXD2Ovbklt2DNWiFhEDcWdDlIo5Gl53BPRrt+2YiXsvE\niUgo8DxwCZAMXCsiyaU22wncArxVRhJ5qtrbfY31WP5n4GlV7QwcAH5c68Eb4yEjJ4+lm/cysV9i\nvSlR+lHP1kSEhTBrpff+k5yfmkVMRChDOtWPjK8x9U14aAhPX92Lo8eLmDJzbf2da/UMPVlwNUf1\n1PKioxrBkwVX+yiik7xZEjcQ2KyqW1X1OPAOcIXnBqq6XVXXAsVVSVCcep+RwEx30WvAuNoL2ZjT\nzVqRjipM7Bc402xVplFUOBcnt2TumkyOF1bp169aSia8Pz+pOZFhNuG9Mf6qc4tYHrykK4vTsnnz\nm52+DscvvXZ4IA8U/IT04mYUq5Be3IwHCn7Ca4cH+jo0r7aJSwA8+/6mA+dWY/8oEUkBCoGpqjoH\niAdyVLVkpNJ09zjGeEVxsTJzRTpDOsXTtmkDX4dTqyb0S+TDtbtZuHEPY7rXbrXAmvQcsnNtwntj\nAsFNg9vz2cY9PP7RBoZ0iqdj8/oxhtqZ2rHvCK8s3QbA3OJhzD0+7JT1CX4wzp4/z9jQTlX7A9cB\nfxORTtXZWUTuEJEUEUnJzs72ToSm3vtm23527j/KVQE02X1Vnde5Gc1jI5nthSrVBalZhIYIFyQF\n1vyyxgSjkBBh2sReRISF8Mvpaygsqv3S+UCycucB7npjBRc8tZi3vt3JgPZNiAw7NbsUHR7K5NFJ\nPorwJG9m4jIAz658ie6yKlHVDPfnVmAx0AfYB8SJSEkJYrlpquoLqtpfVfs3b968+tEbgzPZfWxk\nGGPOCdCeWxUICw1hXO82LErbw/4jx2s17QWpWQxs35S4BtbvyJhA0KpxFI9f2Z01u3J4btFmX4dT\n54qKlf999wMT/vkV4//xFV9u3sud53di6f0jmX7nEP48oScJcdEITgncE+N7nDJki694szp1OdBF\nRDrgZLSuwSlVq5SINAGOquoxEWkGDAWeVFUVkUXARJw2djcD73slehP0cvML+Hjdbq7sk0h0RP1s\n1zWhXyIvfrGND9ZkcvOQ9rWS5va9R9i05zDXDjyrVtIzxtSNy3q24dPULJ5duJkRSS3o3TbO1yF5\nXd7xImau2MXLS7exfd9R2jaN5pHLk7mqf1tiIk9mkUqPs+cvvFYS57ZbuweYB2wApqvqehF5VETG\nAojIABFJB64C/i0i693duwEpIrIGWITTJi7VXXc/8CsR2YzTRu5lb52DCW4frd1NfkExk+phVWqJ\nrq0akdy6Ua32Ul2QmgXYhPfGBKI/XtGdlrGR/PLd1Rw9Xlj5DgEqO/cYf5mfxuCpn/GH99fTuEEE\nz1/Xl0W/HsEtQzuckoHzZ16NUlU/Bj4utewhj/fLcapES+/3FdCjnDS34vR8NcarpqfsonOLhvX+\nv9EJ/RJ57MNUNmXl0qVl7BmntyA1i66tYutdRxBjgkHj6HCemtSL6178hic+3shjATxXdFk2ZeXy\n0hfbeG9VBgXFxVzUrSW3D+9I/3ZNvDbwuTf5c8cGY3xm857DrNyZw6T+iQH5i10dY3u1ITREmLWy\nyk1Wy7X/yHFSduznYiuFMyZgDenUjJ8M68DrX+9gUdoeX4dzxlSVr7bs5db/fMtFTy9hzuoMruqf\nyGe/Op8XburPgPZNA/Z7PjDKC42pYzNW7CI0RPyyDURtax4byYizmzNnVQaTRycRegYDGn+2IYti\nxSa8NybA/WZ0El9s2suUmWuZ94vhNI0JvE5KBUXFfLxuNy8s2cr6zEPEx0Twy1Fnc8Ogs4hvGOnr\n8GqFlcQZU0phUTGzV2ZwQVILWsRG+TqcOjG+byI/HMrnqy17zyidBalZtG4cRfeERrUUmTHGF6LC\nQ3n66t7kHD3Ob2evC6jZHHLzC3hxyVbOf3IRP39nNfkFRTwxvgdfPjCSn4/qUm8ycGAlccac5vPv\ns8nOPVYvx4Yrz4XdWtAoKozZKzM4r0vNhuTJLyjii03O9GSBWjVhjDkpuU0jfn1xElM/2cjslRlM\n8MNZa+asymDavDQyc/Jo0SiS5NaxpGzPIfdYIYM6NuWxcd25IKlFvZkysTTLxBlTyoyUdJo1jGBk\n1+AZqDYqPJTLerXhvZUZPDaukIY16Jm1dNNe8gpswntj6pPbz+vIwg17eHjuegZ2aOpXHZbmrMrg\nwdnryCsoAiDr0DGyDh2jb9s4/nhFd3okNvZxhN5n1anGeNh3+BifbshiXO8EwkOD69djQt9E8gqK\n+Hjd7hrtvyA1i9jIMAZ1tAnvjakvQkOEv0zqBcCvp6+hqNh/qlX//L+NJzJwnrJyjwVFBg4sE2fM\nKeaszqSwWLmqf9vKN65n+p4VR4dmMTWahquoWPlsozPhfUSYfa0YU5+0bdqAR8aew7fb9/PSF1t9\nHQ4AX23Zy+6D+WWuy8zJq+NofMe+bY1xqSozUnbRK7ExSa3OfLy0QCMijO+TwNdb97Nr/9Fq7bt6\n1wH2Hj5uVanG1FMT+iYw5pxWPDU/jdTMQz6L4/CxQn733jque/GbcnvSt/GDienrimXijHF9l3GI\njT/kMjEIS+FKlAypMmdV9caMm5+aRViIMMImvDemXhIR/m98DxpHR/DLd50en3Xt8++zGf30Et76\ndic/GdaBJ67sQXT4qVMi+svE9HXFMnHGuKan7CIyLISxvdr4OhSfadu0AYM6NmX2qoxqDSmwIDWL\nQR3jaRwd7sXojDG+1DQmgmkTe5KWlctf5qfV2XEPHi1g8ow13PzKt0SFhzDzziH8/rJkJg1oyxPj\ne/jlxPR1xXqnGoMzPMb7qzMYfU6roM+IjO+byJSZa1m5M4d+7ZpUuv2W7MNszT7CzYPbez848Rhr\nDAAAIABJREFUY4xPXdC1BTcMOouXlm7jgq4tGNKpmVePtyA1i9+9t459R47zsws6ce/ILkR5lL75\n68T0dcVK4ozB+aI4lF/IpCCuSi1xaY/WRIWHMKuKHRxKJry/sJtVpRoTDH57aTfax8fwm+lrOJRf\n4JVj7D9ynPveXsXt/02haUwEc+4eyuTRXU/JwBnLxBkDOFWpCXHRDOlkw2M0jAxjzDmt+HBNZpXa\nvSxIzSK5dSMSm/jP+FHGGO9pEBHG01f3Jiv3GA+/v75W01ZVPlybyUV//ZxPvtvNL0edzdx7hgXN\nkCHVZZk4E/Qyc/JYunkvE/ol1ttRvatrQr9EDuUX8tmGiie/3nv4GCt3HrBeqcYEmd5t47h3ZGfe\nW5XBh2szayXNPbn53PnGCu55axUJTaL54N5h/HxUFxu2qAJ2ZUzQm7UiHVW4yg+nlPGVIZ2a0apR\nVKVjxi3csAdVLBNnTBD62QWd6dU2jt+99x0/lDNmW1WoKrNWpHPRX5ewKC2bBy7pyuy7htC1lc3B\nXBnLxJmgVlyszFiRzuCO8X41nYyvhYYI4/oksNidR7Y881OzSIiL5pw29mVrTLAJDw3h6Um9OFZY\nxOSZa6rVo71EZk4et766nF/PWEPnFg355Ofncef5nQgLshlzasp6p5qg9u32/ezcf5RfjOri61D8\nzoS+Cfzr8y3MXZPJj4d1OG193vEilm7O5ur+bW3Ce1OnCgoKSE9PJz+/5qU/pva8PiGRnKMFpKz5\nrlrzLh85VsjBvAJu7BrGz/qcRUxEGMf37mLDXi8G62eioqJITEwkPLxmoyJYJs4EtRkp6TSMDOOS\n7q19HYrf6dIylp6JjZm1Ir3MTNwXm7LJLyjmouRWPojOBLP09HRiY2Np3769/QPhB1SV7fuOcuRY\nIR1aNKy0B+mxwiIyDuRRcKyQDpFhJDSJJjIs+Hqdqir79u0jPT2dDh1O/46tCiuvNEHr8LFCPl63\nm8t7tSY6Ivi+QKpifJ8EUncfYsPu06fZWZCaRWxUGOd2bOqDyEwwy8/PJz4+3jJwfkJESGwSTYjA\nrv1HKS6nWlVV2Xv4GJuyDpN3vIiEuGg6NIsJygwcONctPj7+jEqUvZqJE5ExIpImIptF5IEy1g8X\nkZUiUigiEz2W9xaRZSKyXkTWisjVHuteFZFtIrLaffX25jmY+uujtZnkFRQF5WT3VTW2dwJhIXJa\nB4eiYmXhxj1ckNSCcGu7YnzAMnD+JTw0hIQm0eQVFLHn0OntaI8VFLE1+wiZOXnERIbRpWUs8Q0j\ng/4+nun5e606VURCgeeBi4B0YLmIzFXVVI/NdgK3AL8ptftR4CZV3SQibYAVIjJPVXPc9ZNVdaa3\nYjfBYXpKOp2ax9CnbZyvQ/FbTWMiuKBrC+aszuT+MV1PNDZeufMA+47YhPfGmJMaR0fQpEEhe3Lz\n2X/kOIXFxYSHhhATEcqh/EJEILFJA5o0CA/6zFtt8ea/0AOBzaq6VVWPA+8AV3huoKrbVXUtUFxq\n+fequsl9nwnsAZp7MVYTZLZkH2bFjgNMskb5lZrQN5Hs3GN8sflka+MFqVmEhwojkuzX0gSf7du3\n0717d6+kvXjxYi677DIA5s6dy9SpU71yHG+JiXSqRguLnT/rBUXF5OQVEBkWwtktY2kaE1Hl79zq\nXudXX32VzMyKx6x79dVXueeee6qcpr/zZiYuAdjl8TndXVYtIjIQiAC2eCx+3K1mfVpEIsvZ7w4R\nSRGRlOzs7Ooe1tRzM1LSCQ0RruwbvHPuVdXIri2IaxDOrBVOlaqqnpjwPjYquOeZNYFhzqoMhk5d\nSIcHPmLo1IXMWZXh65CqZOzYsTzwwGktkfzX2unE/rMPPV5qT9d3hhC3ec6JVUXF6vWmF1XJxHlL\nYWGhT47r141ZRKQ18Dpwq6qWlNY9CHQFBgBNgfvL2ldVX1DV/qrav3lzKy0wJxUWFTN7ZToXJDWn\nRWyUr8PxexFhIYzt1Yb5qVkczCtgS/Zhtu09wsVWlWoCwJxVGTw4ex0ZOXkokJGTx4Oz151xRq6w\nsJDrr7+ebt26MXHiRI4ePcqjjz7KgAED6N69O3fccceJcdOeeeYZkpOT6dmzJ9dccw0AR44c4bbb\nbmPgwIH06dOH999//7RjeJYa3XLLLdx3330MGTKEjh07MnPmyRZF06ZNY8CAAfTs2ZOHH374jM6r\nxtZOhw/uI/xwBoIScTiDxC8eOJGRO15UXEkCZavqdZ45cyYpKSlcf/319O7dm7y8PJYvX86QIUPo\n1asXAwcOJDc3F4DMzEzGjBlDly5dmDJlyoljNWzYkN/97nf06tWLQYMGkZXlzAu9fft2Ro4cSc+e\nPbnwwgvZuXMn4NyTO++8k3PPPZcpU6bwyCOPcPPNN3PeeefRrl07Zs+ezZQpU+jRowdjxoyhoKD2\n55n15hAjGYBni/FEd1mViEgj4CPgd6r6dclyVd3tvj0mIv/h9PZ0xlRoyaZs9uQeY2I/69BQVRP6\nJvLfZTv4eN1uDhw9DsAoy8QZP/DHD9aTmnl67+kSq3bmnJaByCsoYsrMtbz97c4y90lu04iHLz+n\nwuOmpaXx8ssvM3ToUG677Tb+8Y9/cM899/DQQw8BcOONN/Lhhx9y+eWXM3XqVLZt20ZkZCQ5OU7T\n7scff5yRI0fyyiuvkJOTw8CBAxk1alSFx9y9ezdLly5l48aNjB07lokTJzJ//nw2bdrEt99+i6oy\nduxYlixZwvDhwytMq9o+eQB+WFf++vTlUHRqh4aQojwSl0ym6ca3CREgolSWo1UPuKTi6uKqXueJ\nEyfy3HPP8dRTT9G/f3+OHz/O1VdfzbvvvsuAAQM4dOgQ0dHRAKxevZpVq1YRGRlJUlIS9957L23b\ntuXIkSMMGjSIxx9/nClTpvDiiy/y+9//nnvvvZebb76Zm2++mVdeeYX77ruPOXOczGl6ejpfffUV\noaGhPPLII2zZsoVFixaRmprK4MGDmTVrFk8++SRXXnklH330EePGjaveda+EN0vilgNdRKSDiEQA\n1wBzq7Kju/17wH9Ld2BwS+cQp1J9HPBdrUZt6r0ZKenEx0QwsmsLX4cSMHomNqZT8xhmr0xnQWoW\nPRIa07pxtK/DMqZS5ZUA1bRkqETbtm0ZOnQoADfccANLly5l0aJFnHvuufTo0YOFCxeyfr0zOXzP\nnj25/vrreeONNwgLczIy8+fPZ+rUqfTu3ZsRI0aQn59/ooSnPOPGjSMkJITk5OQTpUTz589n/vz5\n9OnTh759+7Jx40Y2bdp0RudWI0Vlz+wixccRgfAazn9anevsKS0tjdatWzNgwAAAGjVqdOLaX3jh\nhTRu3JioqCiSk5PZsWMHABERESfaI/br14/t27cDsGzZMq677jrAyTQuXbr0xHGuuuoqQkNPDpFy\nySWXEB4eTo8ePSgqKmLMmDEA9OjR40R6tclrJXGqWigi9wDzgFDgFVVdLyKPAimqOldEBuBk1poA\nl4vIH1X1HGASMByIF5Fb3CRvUdXVwJsi0hwQYDVwp7fOoSrmrMpg2rw0MnPyaBMXzeTRSYzr4512\nVvXxWHV9Tn/+30Z2H8wnJjKUj9ft9tqx6hsRoVvrWD5c+wMAsVFhzFmVYdfP+FxlJWZDpy4kIyfv\ntOUJcdG8+9PBNT5u6cb5IsLdd99NSkoKbdu25ZFHHjkx/tdHH33EkiVL+OCDD3j88cdZt26dM1/o\nrFkkJSWdkk5J5qwskZEnm4CXVNWqKg8++CA//elPa3wuVVJJiRlPd4eDu05bXNgwgeM3fkBMg4ga\nHbY617mqPK9jaGjoifZs4eEne816Lq9ITExMmWmHhISckl5ISIhX2s15dcYGVf0Y+LjUsoc83i/H\nqWYtvd8bwBvlpDmylsOssZK2FnkFRYDT1uL+WWvZtvcww8+u3XZ4S77P5l+fb+VYYXG9OZYvz+nI\nsSIenO1UDVhGpHJzVmXwaeqeE59z8wvt+pmAMHl00inf0wDR4aFMHp1UwV6V27lzJ8uWLWPw4MG8\n9dZbDBs2jK+++opmzZpx+PBhZs6cycSJEykuLmbXrl1ccMEFDBs2jHfeeYfDhw8zevRonn32WZ59\n9llEhFWrVtGnT59qxzF69Gj+8Ic/cP3119OwYUMyMjIIDw+nRYs6rmm48CH44D4o8Mgwh0cTfvEj\nNKlhBg6qfp0BYmNjT7R7S0pKYvfu3SxfvpwBAwaQm5t7ojq1uoYMGcI777zDjTfeyJtvvsl5551X\n4/OpbTbt1hmYNi/tlC8GgGOFxfz9s838/bPNXj9+fTxWXZ5TXkER0+alWSakCqbNSyO/8PR2RXb9\njL8reT5ru8Q/KSmJ559/nttuu43k5GTuuusuDhw4QPfu3WnVqtWJaryioiJuuOEGDh48iKpy3333\nERcXxx/+8Ad+8Ytf0LNnT4qLi+nQoQMffvhhteO4+OKL2bBhA4MHO6WKDRs25I033qj7TFzPSc7P\nzx6Fg+nQONHJ2JUsr6GqXmc42dEgOjqaZcuW8e6773LvvfeSl5dHdHQ0n376aY1iePbZZ7n11luZ\nNm0azZs35z//+c8ZnVNtEi1neoz6pH///pqSklLr6XZ44CPKu3r/vW1grR7rple+LXddoB7LH85J\ngG1Tf1Srx6qPynvW7foZX9iwYQPdunXzdRjG1IqynmcRWaGq/Svb10rizkCbuOhy21rUdnVgQj08\nlj+cU5s4a5xfFeU963b9jDHGd/x6nDh/N3l0EtHhp07cWxttLYLlWPXxnOoru37GGON/rCTuDHir\nrUWwHKs+nlN9ZdfP+BtVtSnzTMA70yZt1ibOGGNMQNm2bRuxsbHEx8dbRs4ELFVl37595Obm0qFD\nh1PWWZs4Y4wx9VJiYiLp6enYvNgm0EVFRZGYeNpIa1VmmThjjDEBJTw8/LSSC2OCkXVsMMYYY4wJ\nQJaJM8YYY4wJQJaJM8YYY4wJQEHRO1VEsoEdZaxqDBwsZ7fy1pW3vBmwt0YBeldF5+jrtKu7f1W3\nr2y7mq63e187addkX7v31eOte++vv/NV2bYm3/cVrbN77939a+t3vrJtqruuru57O1WtfNR7VQ3a\nF/BCdddVsDzF1+dT3XP0ddrV3b+q21e2XU3X272vnbRrsq/de/+49/76O3+m976Gfwvs3vvBva/K\ndrV57/3tvgd7deoHNVhX0T7+yJvxnmna1d2/qttXtl1N19u9r520a7Kv3fvq8Va8/vo7X5Vta/J9\nX90Y/EGw3fuqbFdv731QVKfWBRFJ0SoMzGfqH7v3wcvuffCyex+c/O2+B3tJXG16wdcBGJ+xex+8\n7N4HL7v3wcmv7ruVxBljjDHGBCAriTPGGGOMCUCWiTPGGGOMCUCWiTPGGGOMCUCWiTPGGGOMCUCW\niasjIhIjIikicpmvYzF1R0S6ici/RGSmiNzl63hM3RGRcSLyooi8KyIX+zoeUzdEpKOIvCwiM30d\ni/E+92/7a+7v+vV1fXzLxFVCRF4RkT0i8l2p5WNEJE1ENovIA1VI6n5guneiNN5QG/deVTeo6p3A\nJGCoN+M1taeW7v0cVb0duBO42pvxmtpRS/d9q6r+2LuRGm+q5nMwHpjp/q6PrfNYbYiRionIcOAw\n8F9V7e4uCwW+By4C0oHlwLVAKPBEqSRuA3oB8UAUsFdVP6yb6M2ZqI17r6p7RGQscBfwuqq+VVfx\nm5qrrXvv7vcX4E1VXVlH4ZsaquX7PlNVJ9ZV7Kb2VPM5uAL4RFVXi8hbqnpdXcYaVpcHC0SqukRE\n2pdaPBDYrKpbAUTkHeAKVX0COK26VERGADFAMpAnIh+rarE34zZnrjbuvZvOXGCuiHwEWCYuANTS\n770AU3G+4C0DFwBq63feBLbqPAc4GbpEYDU+qN20TFzNJAC7PD6nA+eWt7Gq/g5ARG7BKYmzDFzg\nqta9dzPw44FI4GOvRma8rVr3HrgXGAU0FpHOqvovbwZnvKa6v/PxwONAHxF50M3smcBX3nPwDPCc\niPwIH8y1apm4OqSqr/o6BlO3VHUxsNjHYRgfUNVncL7gTRBR1X047SBNEFDVI8Ctvjq+dWyomQyg\nrcfnRHeZqf/s3gcvu/fBye67AT99DiwTVzPLgS4i0kFEIoBrgLk+jsnUDbv3wcvufXCy+27AT58D\ny8RVQkTeBpYBSSKSLiI/VtVC4B5gHrABmK6q630Zp6l9du+Dl9374GT33UBgPQc2xIgxxhhjTACy\nkjhjjDHGmABkmThjjDHGmABkmThjjDHGmABkmThjjDHGmABkmThjjDHGmABkmThjjDHGmABkmThj\nTI2IyNMi8guPz/NE5CWPz38RkV9VksZXVTjOdhFpVsbyESIypJx9xorIA5Wk20ZEZrrve4vIpdXc\n/xYRec59f6eI3FTZuVR2DjVNxxvc2D70dRzGmPLZ3KnGmJr6EpgE/E1EQoBmQCOP9UOAX1aUgKqW\nmQmrohHAYeC0jKCqzqWS0dRVNROY6H7sDfQHPq7q/qXSqunk9iPwOIczSMcYE4SsJM4YU1NfAYPd\n9+cA3wG5ItJERCKBbsBKABGZLCLLRWStiPyxJAEROez+DBGRf4jIRhFZICIfi8hEj2PdKyIrRWSd\niHQVkfY4k4z/UkRWi8h5noGVKiV7VUSeEZGvRGRrSboi0l5EvnOn0HkUuNpN6+pS+18uIt+IyCoR\n+VREWpa+ECLyiIj8xi3dW+3xKhKRdmWlUdY5lKTjptlbRL52r9l7ItLEXb5YRP4sIt+KyPelz93d\nprWILHHT/a5kGxEZ417HNSLymbtsoIgsc2P7SkSSykgvRkRecY+5SkSuKPepMMbUGcvEGWNqxC3J\nKhSRs3BK3ZYB3+Bk7PoD61T1uIhcDHQBBuKUePUTkeGlkhsPtAeSgRs5mTkssVdV+wL/BH6jqtuB\nfwFPq2pvVf2iknBbA8OAy4Cppc7jOPAQ8K6b1rul9l0KDFLVPsA7wJTyDqKqmW4avYEXgVmquqOs\nNKpwDv8F7lfVnsA64GGPdWGqOhD4RanlJa4D5rlx9AJWi0hzN6YJqtoLuMrddiNwnhvbQ8D/lZHe\n74CF7jEvAKaJSEx518EYUzesOtUYcya+wsnADQH+CiS47w/iVLcCXOy+VrmfG+Jk6pZ4pDMMmKGq\nxcAPIrKo1HFmuz9X4GT4qmuOm3ZqWSVplUgE3hWR1kAEsK2yHURkKHA7znlVOw0RaQzEqern7qLX\ngBkem3hej/ZlJLEceEVEwnHOfbWIjACWqOo2AFXd727bGHhNRLoACoSXkd7FwNiSUkIgCjgLZw5J\nY4yPWEmcMeZMfImTaeuBU536NU4p2hBOtlUT4ImSEipV7ayqL1fzOMfcn0XU7J/PYx7vpZr7Pgs8\np6o9gJ/iZGDK5WbUXgYmqerhmqRRBRVeD1VdAgwHMoBXK+ks8RiwSFW7A5eXE5vglOCV3MOzVNUy\ncMb4mGXijDFn4iucKsr9qlrklu7E4WTkSjJx84DbRKQhgIgkiEiLUul8CUxw28a1xGnwX5lcILYW\nzqGytBrjZIYAbq4oEbfkawZONej3VUijzOOq6kHggEd7txuBz0tvV0Ec7YAsVX0ReAnoi5PBHi4i\nHdxtmpYR2y3lJDkPp12iuPv2qWosxhjvsUycMQFKRA6LSEcfh7EOp1fq16WWHVTVvQCqOh94C1gm\nIuuAmZyecZkFpAOpwBs4HSIOVnLsD4Ary+rYUAOLgOSSjg2l1j0CzBCRFcDeStIZgtMe8I8enRva\nAAuBL8pIo6xz6C8iS3Eye9NEZC1OW8JHq3IibhvFNGCNiKwCrgb+rqrZwB3AbBFZg1O9exYwGnjC\n3fa0Uj1xOp+8jlPNulZE1uOU3pXerr2IqIiEuZ8/EZEKM701ISLr3aphv+Z2jlnq6zhM/Saq6usY\njPEJEdkOtMSpkirAKTm6U1V31UK6P1HVT8tZPwJ4Q1UTz+Q49Y2INFTVwyISD3wLDFXVH3wdV10T\nkVtwnp9h5axfjPP8vFTW+jM8do3TdnvbbgPCVbWwluJ5FUhX1d/XRnp1qbL7aExtsJI4E+wuV9WG\nOL0Xs3DaLvlcSWlGfVTBuX0oIquBL4DHgjEDZ4wx1WGZOGMAVc3HqeZLLlkmIpEi8pSI7BSRLBH5\nl4hEu+uaiciHIpIjIvtF5Au3PdfrOL32PnCrO08ZjsIdluEToI27/rA4Y4s9IiIzReQNETkE3OIx\nfleOiOwWkefEGdOsJC0Vkc7u+1dF5HkR+UhEcsUZk6xTeecrIjNE5AcROSjOeGLneKyLFme2hR3u\n+qUe5z1MnLHEckRkl1vaUDJ22U880jilKsmN9WcisgnY5C77u5vGIbea8Q9uo/lk4HUR+a2IbHHP\nZ4WItHXP8S+lzmWuiJw2qLCI/FNEniq17H1xZ5EQkftFJMNNP01ELiwjjQ7uuYa4n18UkT0e618X\nd9YKEWksIi+79ypDRP4kIqHlXI+L3WMeFGd8vM89r5+7zVMickBEtonIJe6yx4HzgOfcZ+e5MmIu\nXa25WEQeE5Ev3XOdL+4MGJ7blpd2qefsR+KME3fIvXePlD6+RxwnnglxxqU77PFScatEy3sWReQO\n4HpgirvPB+7y7SIyyn0fKSJ/E5FM9/U3ccYoLJlxIl1Efi0ie9z7cmsF8d4izjiCue41v95j3e0i\nssFdlyoifd3lD3g8o6kicmUF6XcVZwzE/e69n1TetsZUmaray15B+QK2A6Pc9w1whnH4r8f6p3FG\n7W+K04brA5xelgBP4IzxFe6+zuNk84QT6ZZz3BE4VUSeyx7BqdIdh/PPVTTQDxiE006pPc5wDr/w\n2EeBzu77V4F9OGOxhQFvAu9UEMNt7jlFAn8DVnusex5YjDNcSChOO69IoB1OQ/xr3XOOB3q7+yzG\nqToqSeMWYGmpWBe41zLaXXaDm0YY8GvgByDKXTcZp21dEk7PyF7utgOBTCDE3a4ZcBRoWcY5Dgd2\nedyXJkAe0MZNdxfQxl3XHuhUzrXaCfRz36cBW4FuHuv6uO/fA/4NxAAtcKqEf1r6ergxH8IZKiUM\n+Ll773/isW0BzhAlocBd7jlLWde6jHjbu9c7zGP7LcDZOM/VYmBqBdv+pFR6ns/ZCJyeyCFAT5zS\n63FVTctdfgfO2HSNqvAsvgr8qYLf20dx2mO2AJrjNIl4zCPWQnebcOBSnGelSRkxxbj3JMn93Bo4\nx31/FU7HjwE4z2JnoJ3Hujbu9bgaOAK0LuOex+A8b7e697wPTtvIZF9/D9orsF8+D8Be9vLVy/1j\ncBjIcf9oZgI93HXifiF38th+MLDNff8o8H7JH7cy0q1JJm5JJfH+AnjP43PpTNxLHusuBTZW8TrE\nuWk1dv8Y5QG9ytjuQc/jl1p3yh9sys7EjawkjgMlx8XJLF1RznYbgIvc9/cAH5ezneBksoa7n2/H\nGbAW9w/xHmAUThuuiuJ6HfgV0MqN60mcmRY6uM9OCE7bymO4GVR3v2txhu4o/Qf9JmBZqTh3cWom\nbrPH+gbu9WtV1rUuI972nJ6Z+r3H+ruB/1WwbbmZuDKO9TecwYqrmtYw97qfXdmz6PFcV5SJ2wJc\n6rFuNLDd4/csryQed9kenEGXSx83xr2XEzzvobtuHvDzKv4urcZ9bkvd86uBL0pt+2/g4aqkay97\nlfey6lQT7MapahzO2Fj3AJ+LSCuc/+obACvc6rQc4H/ucoBpwGZgvlsFU+Fk6VV0SocKETlbnCrb\nH8SpYv0/nFKc8ni2ITuKM6juaUQkVESmutVAh3D+KOKm3QznWmwpY9e25SyvqtLn9xu3iuqge30b\nc/L8KjrWazileLg/Xy9rI1VVnNkRrnUXXYdTQomqbsbJFD8C7BGRd8TpRVqWz3EyBMNxBiheDJzv\nvr5QZxDhdjilPbs9npd/45QQldYGj2vhxpleapsfPNYfdd+WeT+rqErPRmVE5FwRWSQi2SJyECcz\nW9Ez6blvW2A6cLO6w69U8ixWRRtgh8fnHe6yEvv01E4WZZ67qh7ByWjdiXMPPxKRru7qcp9FEblJ\nnJ7FJfe8ezmxtwPOLdnO3fZ6nH8MjKkxy8QZA6gzxtlsnJ6qw3CqOvJwqlTi3FdjdTpBoKq5qvpr\nVe0IjAV+JSfbVFXW5bu89aWX/xOn2qmLqjYCfkv1B6oty3XAFTilUI05OeK/4Jx3PlBWe7pd5SwH\np9Sygcfnsv44nTg/cYbTmAJMwqneisMZUqTk/Co61hvAFSLSC2d+1jnlbAfwNjBRnHHTzsUZysQJ\nRvUtdXoOtnNj+3M5aXyOU10+wn2/FBiKk4krGbttF05JXDOP56WRqp5TRnq7cWZwAEBExPNzFVT2\nfJ2JytJ+C6eJQVtVbYzTpKDSZ1KcNpVzgL+p6iceqyp6FqsSTybO/Stxlrus2lR1nqpehFOVuhFn\nijIo51l0n6kXcf75i3ef4e8o+3rsAj73eDbiVLWhqt5Vk1iNKWGZOGNw/pCKM6l3E2CDW7ryIvC0\nuAPTijNI7Wj3/WUi0tn9A3wQJ/NX7CaXBVQ0flsWEC/O1EoVicVpp3PYLRWorS/8WJwMxz6cjNeJ\nuTLd834F+Ks4HS5CRWSw21j8TWCUiExyG8LHi0hvd9fVwHgRaeA2gv9xFWIoBLKBMBF5CGjksf4l\n4DER6eLem57iDD2CqqbjTCv1Os7cpHnlHURVV+FkTF/CmUs0B0BEkkRkpHte+TgZ9uJy0tjkrr8B\n5w/xIZx7OAE3E6equ4H5wF9EpJE4nVw6icj5ZST5EdBDRMaJ0/ngZ1SvRKay5+tMVJZ2LM7Azvki\nMhAnE1YVr+BU7z9ZRnplPotVjOdt4Pci0lyczhoP4WTyq0VEWorIFeJ0PDqG08yi5Hl4CfiNiPRz\nn8XObgYuBieTme2mcStOSVxZPgTOFpEbRSTcfQ0QkW7VjdUYT5aJM8HuA3EGMz0EPI414txoAAAg\nAElEQVRT1bPeXXc/TpXp125Vz6c4DeLBmfvzU5wv+2XAP1R1kbvuCZw/LDlycq7JE1R1I84fn63u\nNuVV4/0G549kLk6GsvTE7DX1X5xqpwycwXW/LrX+NzidCpYD+3FKqEJUdSdOW7tfu8tX43Q4AKcT\nyHGcP7qv4VZbVmAeTvX0924s+Zxa3fpXnKq3+Tj35mWcRvklXsNpYF9mVWopb+GU9LzlsSwSmIqT\nwfsBp9rzwQrS+Bynam6Xx2fBGZS4xE0486Km4rTvm4lTqnMKdQZBvgqnbd0+nB7RKZw6NVhF/o5T\nunhARJ6p4j5VVVnadwOPikguToZpehXTvQZnUGPPHqrnUfmz+DLOIMw5IlJWieufcK7dWpxndqW7\nrLpCcNo9ZuI82+fj/tOkqjNwvhvewvldnAM0VdVU4C84v/9ZOM/jl6el7KSRizP/7DXuMX7A+b2K\nrEGsxpxgg/0aYwKOiAzHKXFppwH+JSbO8CXpwPUe/wgYY0ylrCTOGBNQxJmf9Oc4vXEDMgMnIqNF\nJM6tzi1p61i6FMoYYypkmThjTMBw2xDl4FRT/s3H4ZyJwTg9HvcCl+P0ki63bZ8xxpTFqlONMcYY\nYwKQlcQZY4wxxgSgejvJtqdmzZpp+/btfR2GMcYYY0ylVqxYsVdVm1e2XVBk4tq3b09KSoqvwzDG\nGGOMqZSI7Kh8K6tONcYYY4wJSJaJM8YYY4wJQJaJM8YYY4wJQEHRJs4YY4wx1VNQUEB6ejr5+fm+\nDqXeioqKIjExkfDw8Brtb5k4Y4wxxpwmPT2d2NhY2rdvj4j4Opx6R1XZt28f6enpdOjQoUZpWHWq\nMcYYY06Tn59PfHy8ZeC8RESIj48/o5JOK4kzxhhjPMxZlcG0eWlk5uTRJi6ayaOTGNcnwddh+YRl\n4LzrTK+v35XEicgYEUkTkc0i8kAZ658WkdXu63sRyfFFnMYYY+qfOasyeHD2OjJy8lAgIyePB2ev\nY86qDF+HZsxp/CoTJyKhwPPAJUAycK2IJHtuo6q/VNXeqtobeBaYXfeRGmOMqY+mzUsjr6DolGV5\nBUVMm5fmo4iC1/bt2+nevbtX0l68eDGXXXYZAHPnzmXq1KleOY63+Vt16kBgs6puBRCRd4ArgNRy\ntr8WeLiOYjPGGFPPZebkVWu5OSlQq6HHjh3L2LFjfR1GjfhVSRyQAOzy+JzuLjuNiLQDOgALy1l/\nh4ikiEhKdnZ2rQdqjDGm/mkTF12t5cbhrWrowsJCrr/+erp168bEiRM5evQojz76KAMGDKB79+7c\ncccdqCoAzzzzDMnJyfTs2ZNrrrkGgCNHjnDbbbcxcOBA+vTpw/vvv3/aMV599VXuueceAG655Rbu\nu+8+hgwZQseOHZk5c+aJ7aZNm8aAAQPo2bMnDz/sH+VH/lYSVx3XADNVtaislar6AvACQP/+/bUu\nAzPGGBOYfn1RF349Yy2l/2ic16WZT+LxF3/8YD2pmYfKXb9qZw7Hi4pPWZZXUMSUmWt5+9udZe6T\n3KYRD19+ToXHTUtL4+WXX2bo0KHcdttt/OMf/+Cee+7hoYceAuDGG2/kww8/5PLLL2fq1Kls27aN\nyMhIcnKc5vKPP/44I0eO5JVXXiEnJ4eBAwcyatSoCo+5e/duli5dysaNGxk7diwTJ05k/vz5bNq0\niW+//RZVZezYsSxZsoThw4dXmJa3+VtJXAbQ1uNzorusLNcAb3s9ImOMMUEjNDQEBZrGRCBAm8ZR\nJLWK5Z3lu/jPl9t8HZ7fKp2Bq2x5VbVt25ahQ4cCcMMNN7B06VIWLVrEueeeS48ePVi4cCHr168H\noGfPnlx//fW88cYbhIU5ZVTz589n6tSp9O7dmxEjRpCfn8/OnWVnKkuMGzeOkJAQkpOTycrKOpHO\n/Pnz6dOnD3379mXjxo1s2rTpjM6tNvhbSdxyoIuIdMDJvF0DXFd6IxHpCjQBltVteMYYY+orVeVf\nn2+lS4uGzPvFcEJCnOEfjhUWcd/bq/jj/7N333FV1u0Dxz9fNqKAWxkqmuLeI2ea5sptmSszS21q\ny8pfZT5WT5Y+Dc1SszItV+6ZuXKlKYp7b8GBi6XIOt/fHzcoICjgOdwHuN6v13kdzn3uc+4LRLj4\njutadohbcYm82uoRkyPNeQ8aMWs6dj2h6awb9PV2Z+7Qxtm+btoSHEopXnnlFYKCgvD392f06NF3\n6qytWLGCTZs2sWzZMj777DP279+P1poFCxYQGBiY6n2Sk7P0uLq63vk4eapWa83IkSMZOnRotj8X\nW7CrkTitdQLwGrAaOAzM01ofVEqNUUqlXHXYG5ijk7+6QgghxEPadPwqhy9GMqRF+TsJHICrkyOT\n+talW20fxq0+yrjVR5BfP6mNaBeIu7NjqmPuzo6MaBeYwSsy59y5c2zbZozXzJo1i2bNmgFQrFgx\noqOj76xZs1gsnD9/nlatWvHFF18QERFBdHQ07dq1Y+LEiXf+vYKDg7MVR7t27fj555+Jjo4GIDQ0\nlLCwsIf63KzB3kbi0FqvBFamOTYqzePRORmTEEKIvG/y3ycp5elG19r37qdzcnTgf71q4+7iyKQN\nJ7kZm8jHnatKMdwkybtQrb07NTAwkEmTJjFo0CCqVq3Kyy+/zI0bN6hevTqlSpWiQYMGACQmJtK/\nf38iIiLQWjNs2DC8vb356KOPeOONN6hZsyYWi4WAgACWL1+e5Tjatm3L4cOHadzYGFUsWLAgv/32\nGyVKlHioz+9hqfzw10T9+vV1UFCQ2WEIIYSwU3vPh9N10lY+6FiFwS3KZ3ie1ppPlh/m562neaa+\nP//tUQNHh7yZyB0+fJgqVaqYHUael97XWSm1S2td/0GvtbuROCGEECKnTd54Ek83J/o0KnPf85RS\nfNSpCgVdHZmw/gQx8Yn8r1ctnB3tanWSyCckiRNCCJGvnboSzZ8HL/FKywoUdH3wr0WlFG+1DcTd\nxYkv/jxCTHwiE/vUwS3NmjAhbE3+dBBCCJGv/bj5NM6ODgxsEpCl173csgJjulZjzaHLDJ4RxK24\nBBtFKET6JIkTQgiRb4VF3WbB7hCequdH8UJJpSX2zYOvq8Nob+N+37wMXz+gcTnGPVWTrSeu8tzP\nO4i6HZ9DkQshSZwQQoh87JetZ0hItDCkedJmhn3zYNkwiDgPaON+2bD7JnJP1/dnQp86BJ8Lp9+0\nf7lxMy5nghf5niRxQggh8qWomFjWbQ/inYCzlDv6Eyx+BZa8CvFpitbGx8C6Mfd9r041fZjybD2O\nXIqi99TthEXdtmHkQhhkY4MQQoi8zWIxRtSuHDFuYca92+XD/EUMXMC4FSwJiRmMokWEPPAyrauU\n5JeBDXjx1yCembKd319shI+3u1U/lfzkzJkzdOrUiQMHDmTq/OnTp9O2bVt8fHzue05QUBDfffed\ntcI0lSRxQggh7N++ecZoWEQIePlB61FQs1fqcywWCD8LV47ClcNJ90fgyjGIv3n3vIKlsBQPZKFu\nRZT3I7zYvSMUD4QCRYw1cBHn772+gxNcPQHF7t9yq+kjxZj5QkOe/2UnT0/exqzBjShb1MMKX4Bc\nIDP/RjY0ffp0qlevft8kzlYSEhLu9GvNSZLECSGEsG/J69SSpzkjzsPS1+HiXihQNEXSdgwSUkyF\nFioNxStD3QFQorLxcfFAcC/M/J3nee/wPmb0bghli999TetRqa8F4OhiJHFTWkCHL6BOf7hPp4b6\n5Yowe8ijPPvTvzw9eRu/v9iIiiULWfmLYmfS+zdaNsz4+CESuYSEBPr168fu3bupVq0aM2bMYPz4\n8SxbtoyYmBiaNGnClClTWLBgAUFBQfTr1w93d3e2bdvGgQMHGD58ODdv3sTV1ZV169YBcOHCBdq3\nb8/Jkyfp3r07X375JWB0YRg+fDjLly/H3d2dJUuWULJkSc6cOcOgQYO4evUqxYsX55dffqFMmTIM\nHDgQNzc3goODadq0KZ6enpw+fZpTp05x7tw5vv76a7Zv386qVavw9fVl2bJlODs7P9SXOS3p2CCE\nEMK+ZTQ6lszT10jOilcx7ktUgWKVwN073dMtFk2brzfi5uTIimHN7m2dld6IUrlmsHAInNkM1bpD\np6/BvfB9wz52OYp+0/4l0aKZMagh1X29svqZmypVJ4FV78Ol/RmfHLITEmPvPe7oCn4N0n9NqRrQ\nYWyGb3nmzBkCAgLYsmULTZs2vdN6a9CgQRQpUgSAZ599ll69etG5c2datmzJ+PHjqV+/PnFxcVSu\nXJm5c+fSoEEDIiMjKVCgAL/99htjxowhODgYV1dXAgMD2bJlC/7+/iilWLp0KZ07d+bdd9/F09OT\nDz/8kM6dO/PUU0/x3HPP8fPPP7N06VIWL17MwIEDuXr1KkuWLMHR0ZHRo0ezdu1aNmzYwKFDh2jc\nuDELFiygQ4cOdO/eneeee45u3brd/+ucRDo2CCGEyN0SE+D4X/dJ4BS8fxbcspYcrTl8mVNXbvJt\n79rp9z6t2Sv90aMBS+CfCbD+UwgJgh5ToWyTDK9TqWQh/hjamH7T/qXPj9uZ/nxD6pW9f+KXa6WX\nwN3veCb5+/vTtGlTAPr378+ECRMICAjgyy+/5NatW1y/fp1q1arRuXPnVK87evQopUuXvtNb1dPT\n885zrVu3xsvL+J6pWrUqZ8+exd/fHxcXFzp16gRAvXr1WLNmDQDbtm1j4cKFgJE0vvvuu3fe6+mn\nn8bR8W6R5w4dOuDs7EyNGjVITEykffv2ANSoUYMzZ8481NciPZLECSGEsC8RIbB7BuyeCVEXQDmA\nttx7npdflhM4rTWTN57Ev4g7T9YonbW4HByh2ZsQ0AIWvAjTn4Tm78Bj74Fj+r9OyxXzYN5Ljen3\n43ae/elfpj1XnyYVimXtuvbgPiNmQMajpV7+8PyKbF82bZKtlOKVV14hKCgIf39/Ro8eze3bWdsJ\n7OrqeudjR0dHEhKMIs3Ozs53rpfy+P14eKRe75j83g4ODqnez8HBIVPvl1VSYkQIIYT5EhPg6CqY\n9Qx8UwM2fgklq8Izv0PXH8A5zS5PZ3djmjOLdp65QfC5cAY3L49Tdvud+taDoZugVh/Y9CX80gFu\nnMn4dG935g1tjF9hd57/ZScbjoRl77r2rPUoq/0bpXTu3Dm2bdsGwKxZs2jWrBkAxYoVIzo6mvnz\n5985t1ChQkRFRQEQGBjIxYsX2blzJwBRUVHZTqKaNGnCnDlzAPj9999p3rx5tj8fa5OROCGEEOaJ\nCDFG3IJnQmSoUeaj2VvGZoTCZe+e5+BglZ2PkzeepIiHC0/X83+4uF0LQbfvocLjsPwtmNwcnvwK\naj6d7uklPN2YM6QxA37+lyEzg/i2dx06ZnUk0J4l/1tYeXdqYGAgkyZNurMe7uWXX+bGjRtUr16d\nUqVK3ZkuBRg4cCAvvfTSnY0Nc+fO5fXXXycmJgZ3d3fWrl2brRgmTpzI888/z7hx4+5sbLAXsrFB\nCCFEzrIkwvE1sOsXY82b1kYyVP95qNQeHK27gy/Z0UtRtPtmE2+2qcTwNhWt98Y3zhqbHs5vh5q9\noeM4cPNM99TI2/E8/8tOgs/dYNxTtehZz896cVhZegvuhfXJxgYhhBD2LyLUGHHbPRMiQ5JG3d5M\nGnUrZ/PLT9l0EndnRwY0Lvvgk7OicFkYuAI2j4eNXxjJXM+fwO/e38Gebs7MfKEhg2cE8fYfe9l2\n6hrbTl7jQngMPt7ujGgXSLc6vtaNT+RZksQJIYSwHUsinFgLu6bDsT/vjrq1/xwCO9hs1C2t0PAY\nlu65wLONy1LYw8X6F3B0gpbvQ/mWsGAw/NQWWv2fkaQ6OKY6tYCLEz8914Ae329l/q67nSBCw2MY\nudAo4yGJnMgMSeKEEEJYX+QFY8Rt9wxj1M2jBDR9A+o9lyOjbmn9tPk0GngxudG9rZR5FF7aDCve\ngvWfwMkN0GOKsUYsBTdnR8Jj4u95eUx8IuNWH7WbJE5rnX4ZFmEVD7ukTZI4IYQQ2ZeqMK4vVO0B\n108kjbpZkkbd/guBHXNs1C2t8FtxzNl5ji61fPDNiV6m7t7GdOojT8DKd+CHptBlAlTtmuq0i+Hp\nl8a4EB6T7vGc5ubmxrVr1yhatKgkcjagtebatWu4ubll+z0kiRNCCJE997RaCoFtE8ClkDHqVncA\nFAkwN0Zgxraz3IpLZOhjNh6FS0kpqN0H/BvCghdg3gCo+5wxjexi1Bbz8XYnNJ2EzScnEs1M8PPz\nIyQkhCtXrpgdSp7l5uaGn1/2N7dIEieEECJ71v0ndY/RZO5e0ObjnI8nHbfjE5n+zxlaBRancqn0\nd4zaVNEKMOgv+Pu/sOUbOPsP9JwGPrUZ0S6QkQv3ExOfmOol1X1MiDMdzs7OBASYn4SLjEmxXyGE\nEFl3fqcx8paeiNCcjeU+/gg6z/Wbcbz0WAXzgnBygTaj4bmlEHcTprWBfybSrVZpPu9RA19vdxTg\n4+1Gw3KFWX3oMl+tOfbQ66VE3icjcUIIITIv7qbRO3T7D6AcQSfee46XfdQ+S0i0MHXzKeqU8aZh\nQBGzwzHadb28FZa+Dn99CCfW0a3yk3Rz/RbcQsDVD0vjUYwsVpkJ646jteatJyrJejSRIUnihBBC\nZM6pv2HpMAg/Cw0GQ+masOrd1FOqVmi1ZC0rD1zi/PUYPuhY1X4SoQJF4JnfjJIrK0bAqQ13n4s4\nj8PyYXzeaQJKVWbi+hNoDW+3lUROpE+SOCGEEPcXE26MHAXPhCIV4PlVULaJ8ZyTm9VbLVmD1pop\nG09SvrgHbauWNDuc1JQyulP8PRaiL6V+Lj4Gh/Vj+O/w/SgF3204gUbzTttASeTEPSSJE0IIkbHD\ny2HF23DzilG49rH3Ujc6r9nLLpK2tLacuMrBC5F80bMGDg52mvxEX07/eEQIDg6Kz7rVABSTNpzE\nouHddpLIidQkiRNCCHGv6DBYOQIOLYaSNaDvHPCpY3ZUmTZ540lKFHK1m6K56fLyg4jz9x4vZIwc\nGolcdRwU/PD3SbSG99pLIifusrvdqUqp9kqpo0qpE0qp9zM4p5dS6pBS6qBSalZOxyiEEHmW1rB3\nDkxqCEdXwuMfwZANuSqB2x8SwdYT1xjULABXJ8cHv8AsrUelHtVMdiscjv0FGIncJ12r0//RMkze\neJKxfx6RXaviDrsaiVNKOQKTgCeAEGCnUmqp1vpQinMqAiOBplrrG0qpEuZEK4QQeUz4eVj+htHr\n1L8RdPkOilcyO6osm7zxJIVcnejbqIzZodxf8jR0yjWFjV+FPb/DrF5Grb2mb9xJ5BSKKRtPoTWM\n7FBZRuSEfSVxQEPghNb6FIBSag7QFTiU4pzBwCSt9Q0ArXVYjkcphBB5icUCQT/B2tHGSFyHL43d\npw52N1nzQGeu3mTVgYsMaVEBTzdz2nxlSXprCusOgCWvGv8elw5Al4kolwKM6VoNpWDqplNorfm/\njlUkkcvn7C2J8wVSLhAIARqlOacSgFJqK+AIjNZa/5n2jZRSQ4AhAGXK2PlfY0IIYZarx426Zee2\nQflW0PlbKFzW7Kiy7cfNp3BycGBQ03Jmh5J9Lh7w1C9QsrpRk+/aceg9C+Xlx3+6VEMBP24+jdbw\nwZOSyOVn9pbEZYYTUBFoCfgBm5RSNbTW4SlP0lpPBaYC1K9fXxYQCCFESonx8M9Eo8yFsxt0/R5q\n9zXKX+RSV6Ji+WNXCD3q+lLCM/tNxe2CUtDiHShZDRYMhqmt4JmZqDKPMrpLNZRSTNtyGouGjzpJ\nIpdf2dtYeSjgn+KxX9KxlEKApVrreK31aeAYRlInhBAiMy7uhR8fN3qfVmoLr+6EOv1ydQIHMP2f\n08QnWhjSIgcb3dtaYAd4ca0xOje9E+z6FaUUH3euyvNNy/Hz1tOMWX5INjvkU/aWxO0EKiqlApRS\nLkBvYGmacxZjjMKhlCqGMb16KieDFEKIXCn+trGIfmoriLoEvWYY3QMK2Vkx3GyIjk1g5raztKta\nivLFC5odjnWVqAyD10O5ZrBsGKwcgbIkMKpTVQY1DeCXrWf4zzJJ5PIju5pO1VonKKVeA1ZjrHf7\nWWt9UCk1BgjSWi9Neq6tUuoQkAiM0FpfMy9qIYTIBc5thyWvGeuraveDtp8aLaDyiDk7zhF5O4Gh\nj+WhUbiUChSBfvNhzSjYPgnCDqN6zUiaSoWftpwG4OPOdtRiTNicXSVxAFrrlcDKNMdGpfhYA28l\n3YQQQqS1b97dshWePlAs0OjR6eUP/RfCI63NjtCq4hIsTNt8mkYBRahTprDZ4diOoxO0/y+Uqg7L\nhsPUlqg+s/nwyao4qOTNDvrOmjmR99ldEieEEOIh7JtnTLklN6WPDDVu5R+HZ2aCax6bagSW7Anl\nUuRtPu9Zw+xQckbtvlCsEszpB9OeQPWYwv917IRSiqmbTmHRJJUjkUQur7O3NXFCCCEexroxdxO4\nlK4dz5MJnMWimbLpFJVLFaJlpeJmh5Nz/OrDkL+N9XJz+6M2fsHI9pUY2qI8M7ef5aMlB7BYZI1c\nXicjcUIIkZdEhGTteC63/kgYJ8Ki+eaZ2vlv5MmzNAxcaUyt/v056vIB3u/2AyjudHb4pGt1HBzy\n2dclH5EkTggh8hL3IhCTzl4vL7+cjyUHTN54El9vd56sWdrsUMzh7AbdJ0OpGrDmI9RP7Xi/9+84\nqAr88PdJNPCpJHJ5lkynCiFEXnF+J9wOB5XmR7uzu9FsPY8JOnOdoLM3eLF5AM6O+fjXmVLQ5DXo\n9wdEhqB+fJx3K13mlZYVmPXvOT5YvF+mVvMoGYkTQoi8IPwczOkD3mWgyTDY8tXdpuqtR93bnzMP\nmLzxJIULOPNMA/8Hn5wfPNIGBm+A2X1QM7szov3nqJYtmPT3KU5fucm5G7e4GH4bH293RrQLpFsd\nX7MjFg9JkjghhMjtYqNgVm9IiIOB86B4JWgwyOyobOr45SjWHg5jeOuKFHCRX2V3FK1gdHhYOBi1\n6l3eqTOAU1X6sOrw9TunhIbHMHLhfgBJ5HK5fDz+LIQQeYAlEea/AFeOQK/pRgKXD0zZdAo3Zwee\na1LO7FDsj5sn9J4Fzd5CBc/gpTNvUoyIVKfExCcybvVRkwIU1iJ/vgghRG62ZhQcXw1P/g8qPG52\nNDniYkQMS/aE0rdhGYp4uJgdjn1ycIQ2H0Op6lT64yXWuL5DPE4UI4ILuhhfJvRiWXgzs6MUD0lG\n4oQQIrfaNR22fQcNh0KDF82OJsf8tPk0Fg0vNs+jLbasqXpPZjj1xIublFAROCjwc7jKWOdpDCi4\nw+zoxEOySRKnlCpqi/cVQgiR5PQmWPG2sZi93X/NjibHRNyKZ/aOc3SqWRr/IgXMDidXeNblb9JW\nGCmg4njVMotE2bWaq9lqJG67UuoPpVRHle+qLwohhI1dPQFzn4Wij8BTPxs9NfO4xcGhNB27nlpj\n/uJmXCIVS+a97hO2UiDmUrrHiyWGMeKPvZLI5WK2SuIqAVOBZ4HjSqn/KqXyx2pbIYSwpVvXYVYv\nY81T37ng5mV2RDa3ODiUkQv3Exp+t53YpPUnWRwcamJUuUgGhZ6VUsTuXcA7ksjlWjZJ4rRhjda6\nDzAYeA7YoZTaqJRqbItrCiFEnpcYD/MGQMR5Y/dh4XJmR5Qjxq0+Skx8YqpjsrsyC1qPMgo+p+Tk\nhipcjkkuE6i3fwzvzflXErlcyGZr4pRSw5VSQcA7wOtAMeBtYJYtrimEEHma1sYauDOboctEKPOo\n2RHlmAspRuAyc1ykUbMXdJ4AXv6AMu67TITXdkKT1+nvtI5BR4bw+W/LSEi0mB2tyAJbLaTYBswE\nummtU3ZdDlJKTbbRNYUQIu/a/j3s/hWavw21epsdTY7y8XZPNZWa8rjIpJq90u/a0fZTKNecgHmD\neePkYH6f9g79Xnwbp/zcxiwXsdW/UqDW+pM0CRwAWusvbHRNIYTIm47+Cas/gCpdoNWHZkeT40a0\nC8QpzfZKd2dHRrQLNCmiPKZSO9xf30aUV2Weu/gpOyb0J+F2tNlRiUywVRL3l1LKO/mBUqqwUmq1\nja4lhBB516UDsOAFKF0Luk8Gh/w3QtK1tg9e7k64ODmgAF9vdz7vUUNaRlmTly+lh69jd5nnaRKx\ngrCvmpFw6bDZUYkHsNV0anGtdXjyA631DaVUCRtdSwgh8qboMJjdG1wLQZ/Z4OJhdkSmCD4fzrWb\n8XzZsya9pNm97Tg6UXfQN6xcVI9Ge0aSOKUldP4ap7p9zY5MZMBWf9IlKqXKJD9QSpUFZNuLEEJk\nVvxtmNMXbl41EjhPH7MjMs2i3aG4OjnQoUYps0PJFzp2f5Y/m81nT2I5nJa+jGXRyxB30+ywRDps\nlcR9AGxRSs1USv0GbAJG2uhaQgiRt2gNS16FkJ3QYyr41DE7ItPEJVhYvu8CT1QtSSE3Z7PDyTf6\nPfEoB1rPZEJCN9g7Gz31cQiT6VV7Y6s6cX8CdYG5wBygntZa1sQJIezLvnnwdXUY7W3c75tndkSG\njV/CgflGfa+qXcyOxlQbj13hxq14usv6txz3wmOVKNDuY56Ne5+oG5fRU1vB7pnGHxnCLthyhWwi\nEAZEAlWVUi1seC0hhMiaffNg2TCjcC7auF82zPxE7sAC+Pu/UKsPNHvL3FjswKLgEIp6uNCiUnGz\nQ8mXXmxensc7PkPrm59y1CkQlr4Gi4ZCrOxetQc22diglHoRGA74AXuARzFqxz1ui+sJIUSWrRsD\n8Wlqj8XHwLr/pF9PKyeE7ILFr0CZxtD5W8jnracjYuJZeziMvg3L4Cx1y0zzQrMAFNBxuRdfl15D\nl/0zUaG74enpUKq62eHla7b6XzEcaACc1Vq3AuoA4fd/iRBC5KCIe8pY3j2+4XO4fjpn4wk/b+xE\nLVgSnvkNnFxz9vp2aNX+i8QlWKSUiB0Y1CyAUZ2rM/xiO8aXGoeOjYRprSHoF2rr91AAACAASURB\nVJleNZGtkrjbWuvbAEopV631EUCqMgoh7EcGTcFxcoWNX8CE2vBLR2MNUGyUbWOJjYbZfSDhNvSd\nBx7FbHu9XGJhcCjli3lQy8/L7FAEMLBpAP/pUo1Jp0vzVpFJWPwbw/I3jDqGtyPNDi9fslUSF5JU\n7HcxsEYptQQ4a6NrCSFE1lVJZ8OAszt0+Q7ePGBsKoi+bKwBGlcRFg6BkxvAYuXekpZEWDgYwg7C\nU79AicrWff9cKuTGLXacvk73Or6ofD6tbE+ea1KOMV2rsehYHEP1SBJafggHF8HUx+DiXrPDy3ds\nsiZOa9096cPRSqkNgBfwpy2uJYQQWXbzKuyfB55+oICIUGNkrvWou+vhmr9tbCwICYI9v8OBhbBv\nrvGaWr2hdl8oWuHhY1n7MRxdCR3GQcU2D/9+ecSSPRcAZCrVDg1oXA4FfLTkIENoyeT+j+KyeDBM\nawPt/gtuXsaa04iQe/9fCatS2spz2UopR+Cg1jpbf04qpdoD3wKOwDSt9dg0zw8ExgGhSYe+01pP\nu9971q9fXwcFBWUnHCFEXqM1zO0Px/+CIX9DyWqZe138bTi6AvbMhpPrQFvAr6GRzFXrDu7eD36P\ntHbPgKWvQ4MX4cn/Zf31eZTWmjZfbaSIhwt/vNTE7HBEBn7bfpYPFx/g8col+KF7GVyXvQIn1oJy\nBJ1490Rnd+g8QRK5LFBK7dJa13/QeVafTtVaJwJHU3ZsyKykBHAS0AGoCvRRSlVN59S5WuvaSbf7\nJnBCCJHK3jlwZDk8/mHmEzgAZzeo3hP6z4c3D8ETYyA20lgTNL4SzB8Ex9ca06OZcXozLH8TKjwO\n7b/I3ueSRx0IjeTklZsyCmfn+j9als+6V2f9kTBeWniW273mGKNwOs3/gfgYY2ROWJ2teqcWBg4q\npXYAd3p1aK0fVLWyIXBCa30KQCk1B+gKHLJRnEKI/CT8PKx6F8o0gcavZf99PEtD0+HQZBhcCIY9\ns4zivAcWQKHSxohDrb4Zr2+7dhLmPQtFyhvr4Bxt9aM4d1oYHIKLowOdauTfVmO5Rb9GZVEo/m/R\nfob+Fsz025Gku4Ixo93g4qHY6ifHR9l8nS9wPsXjEKBROuf1TCoefAx4U2t9Pu0JSqkhwBCAMmWy\nPCgohMhrLBZY/LIxDdrte3BwfPj3VAp86xq3dp/BsT+N6dZ/voOt34JPXWO6tXpPY5opeZ2QgyM4\nukLfudmbhs3DEhItLNt7gccrl8CrgLTZyg36NiqDUjBy4X6ueRSnWGLYvSdltBtcPBRbbWzYaIv3\nTbIMmK21jlVKDQV+JZ0iwlrrqcBUMNbE2TAeIURu8O9kOLPZWJtTJMD67+/kClW7GrfoMNj/hzFC\nt/IdWPWecU7yNJMlwVg3FBJkjMaJOzafuMrV6DiZSs1l+jQsgwLGLH6KL11+wo3YO89pQD36smmx\n5WU2KTGilIpSSkUm3W4rpRKVUpkpIhMK+Kd47MfdDQwAaK2vaa2TvzumAfWsE7UQIs8KOwJrR0Ol\n9lB3gO2vV7AENH4VXt4KQzcbC7vTrhNKjJV1QulYtDsUL3dnWlWWNlu5Te+GZShQrw/vxr1AiKUY\nFq24rL25pV25ufl7iLxgdoh5jq1G4golf6yMAj9dMVpvPchOoKJSKgAjeesN9E15glKqtNb6YtLD\nLsBhqwQthMibEuONXo8uHsYoXE7XHCtdE+Jupv+crBNKJTo2gb8OXaJnXT9cnaww3S1y3ObjVwm1\nNGNpXLM7x2qqk8xW/4WZ3WHgSvAoamKEeYvNm9Fpw2KgXSbOTQBeA1ZjJGfztNYHlVJjlFLJmyKG\nKaUOKqX2AsOAgTYKXQiRF2waBxf3QOdvoFBJc2LIaD2QrBNK5c8Dl7gdb6G7TKXmWhfCY+45tk9X\n4IXYd+DGGfith3R3sCKbjMQppXqkeOgA1AduZ+a1WuuVwMo0x0al+HgkMNIKYQoh8rqQXbBpPNTs\nbaxVM0vrUbBsmFFqIZmzu3Fc3LEoOIQyRQpQr2xhs0MR2eTj7U5oOoncv7oKSwPH0vnwO6jZvaHf\nfHApYEKEeYutRuI6p7i1A6IwplSFECJnxN2CRUOMkh8dTK7DVrOXMZXr5Q8o416Kn6ZyKeI2/5y8\nRjdps5WrjWgXiLtz6qlwVycHavh6MmxXCT5yGIY++w963gBIiDMpyrzDVmvinrfF+wohRKatHQ3X\nTsCAJfZRxqNmL0na7mPJnlC0RqZSc7nkXcXjVh/lQngMPt7ujGgXSLc6vmw/dY1PV3gy8lIkY09M\n4/pvAykyYKZ1yv3kU1ZvuwWglPoVGK61Dk96XBj4n9Z6kNUvlgnSdkuIfObkemMRdaOXocPYB58v\nTNf+m024OTuy+NWmZocibMhi0SzYHULoynG8YfmVrZ5PUnbgj/gV8TA7NLtiWtutJDWTEzgArfUN\noI6NriWEEHfF3IDFr0KxStDmY7OjEZlw+GIkRy5FyShcPuDgoHi6vj+D3/uK7X6DaBq5gr++GcyX\nqw4THZtgdni5jq2SOIek0TcAlFJFsF13CCGEuGvluxB9GbpPMTYPCLu3KDgUJwdF51rSZiu/8HB1\n4tEXviK69gsMcliB45bxtBz3N3N2nCPRIvX5M8tWidX/gG1KqT+SHj8NfGajawkhhOHgItg/D1qO\nNFphCbuXaNEs2RNKy8DiFPFwMTsckZOUomCX8aBjeHvvLLzcivL+wlh+3XaWjzpVoUmFYmZHaPds\nMhKntZ4B9AAuJ916aK1n2uJaQggBQNQlWP6m0a+0+dtmRyMyadvJa1yOjJU2W/mVgwN0mQhVOvNi\n9BQWNTlDZEw8fX/8l8Ezgjh9NYNC2QKwXdutR4HzWuvvtNbfASFKqfQa2QshxMPTGpa+btRh6z4F\nHKVxem6xMDiEQq5OtKliUiFmYT5HJ+j5E5RvRZ3gD9nwZAQj2gXyz4mrtP16I58sP0TErXizo7RL\ntloT9wMQneJxdNIxIYSwvl3T4fhf8MQYKF7J7GhEJt2KS2D1gUt0rFEaN2cpM5GvOblC79/BrwEu\niwbzqv8ZNoxoSc+6fvy89TQtx2/g13/OEJ9oMTtSu2KrJE7pFLVLtNYWZGODEMIWrp+C1R9AwGPQ\nYLDZ0YgsWHPoMjfjEmUqVRhcPKDvPChRGeb0p8SNPYztWZMVrzenSmlPPl56kPbfbGLD0TCzI7Ub\ntkriTimlhimlnJNuw4FTNrqWECK/siTCopfBwQm6fW+srxG5xqLgUHy93WkUUMTsUIS9cPeG/ovA\nyxd+fxou7qWqjye/v9iIHwfUx6Lh+V92MuDnHRy7HMXi4FCajl1PwPsraDp2PYuDQ83+DHKUrX7i\nvQQ0AUKBEKARMMRG1xJC5Ff/TIDz26HjOGkmn8tciYpl8/GrdK3tg4ODtNkSKRQsbnRacfOCmT3g\nyjGUUjxRtSSr32jBR52qsufcDdp+vYm3/9hLaHgMGggNj2Hkwv35KpGz1e7UMK11b611Ca11Sa11\nX621jH8KIazn0gFY/xlU6SLtrHKhpXsvkGjRUuBXpM/Lz0jklAPM7Abh5wBwcXLghWYBbBzRCg9X\nx3tqysXEJzJu9VEzIjaFrXanuimlXlVKfa+U+jn5ZotrCSHyoYRYWDgE3AtDp29AGqbnOouDQ6nu\n60nFkoXMDkXYq6IV4NlFEBcNM7pC1OU7TxX2cOFWbGK6L7sQHpNTEZrOVtOpM4FSQDtgI+AHRNno\nWkKI/GbDfyHsIHT9DjyKmh2NyKITYVHsD42gW20ZhRMPUKo69FtgJHAzu8Ot63ee8vFOvyNLRsfz\nIlslcY9orT8CbmqtfwWexFgXJ4QQD+fsNtj6LdR9Diq1MzsakQ0Ld4fioKBLbWmzJTLBvwH0mQXX\njhubHWKNMaER7QJxT1OaxtlRMaJdoBlRmsJWSVxyVb5wpVR1wAsoYaNrCSHyi9goWDQUvMtAO+nk\nlxtZLJoley7QvGJxShRyMzsckVuUbwlPT4cLwTCnL8TfplsdXz7vUQNfb3cU4OrkgMWiqe7rZW6s\nOchWSdxUpVRh4ENgKXAI+MJG1xJC5BerPzAWOHefAq6ylio32nHmOqHhMbKhQWRd5Seh2w9wehPM\nfx4S4+lWx5et7z/O6bFPsvndVni6O/Pm3D35piiwrXanTtNa39Bab9Jal0/apTrFFtcSQuQTx1bD\n7l+h6TAo29jsaEQ2LQ4OpYCLI22rSZstkQ21noGO4+HoSlj8CljuJmslPN34vEcN9odGMHHdcROD\nzDnSRUEIYf9uXoMlr0GJatDqA7OjEdl0Oz6RFfsv0r56KQq4yK8fkU0NB0NsJKwbA9GXja4tESHg\n5Uf71qN4ql4g3204QcvKJahbprDZ0dqUlDcXQtg3rWH5GxBzA3pMMXosilxp3eEwom4nyFSqeHjN\n34aK7eH0Rog4D2jjftkwPil/iNJe7rw5dw83YxPMjtSmJIkTQmTOvnnwdXUY7W3c75uXc9c9vBQe\n/wBK1ciZawqbWBQcSklPV5pUKGZ2KCIvCDt477H4GNw3fcZXvWpx7votPl1xOOfjykE2Gc9WSvVI\n53AEsF86NwiRC+2bB8uGQXxSEc2kv3gB23ZLiAiBlSPA/1FoMsx21xE2d/1mHH8fDWNQswAcpc2W\nsIaIkAyPNypflCEtyjNl4ynaVClB6yp5cw2mrRYlvAA0BjYkPW4J7AIClFJjtNYzbXRdIYQtrBtz\nN4FLFh8Di1+GoF+MptXuhY2bm3eKx94pjhU2eiE6OKZ/jZT2zYN1/0n6Ia2gaufMvU7YreX7LpBg\n0VLgV1iPl1/SVGo6x4G3nqjExqNXeG/BPla/0YKiBfPeUgxbJXFOQBWt9WUApVRJYAZGwd9NGB0d\nhBC5RUZ/8VoSjOQq/Dxc3Ae3w40WORlS4OaZJuErnDrhu3oc9s6GxLik12hY/yl4lJAeqbnYouBQ\nKpcqRFUfT7NDEXlF61GpZwgAHF2N44CrkyPf9K5Nl4lbGblwP1OerYfKYy36bJXE+ScncEnCko5d\nV0rFZ/QiIYSdcikIcel0zvPyh4HLUx9LiDOSuZgbEJN8fyPFsTTHI87f/VhnUNspPsYYDZQkLlc6\nffUmwefCGdmhstmhiLwk+efBujHGH5pKQcGSUL3nnVMql/JkRLtAPlt5mD92hdCrvr9JwdqGrZK4\nv5VSy4E/kh73TDrmAYTb6JpCCFv45zsjgXNwMkbekjm73/mLNxUnFyhYwrhlhcViXGdsWUDf+3xG\no4HC7i0ODkVJmy1hCzV73U3m9s+HBS9A8EyoN/DOKS80C2D9kTD+s/QgjcsXxb9IAXNitQFb7U59\nFZgO1E66zQBe1Vrf1Fq3stE1hRDWtn8+/PUBVO0KXScZI28o477zBOuOjDk4GGvmktaz3COj48Ku\naa1ZvCeUJhWKUtor/zQmFyao3hPKNoW1/4Fb1+8cdnBQjO9VCweleHPuHhIt6fyRmEvZqmOD1lrP\n11q/mXSbr7XO1FdNKdVeKXVUKXVCKfX+fc7rqZTSSqn61otcCHHHqY2w6CXjh2L3qVCrN7x5AEaH\nG/e2mtpsPcoY5Uspo1E/Yfd2n7vB2Wu3ZEODsD2loMOXxtKNDal7K/t6uzOmWzWCzt5gyqaTJgVo\nfTZJ4pRSPZRSx5VSEUqpSKVUlFIqMhOvcwQmAR2AqkAfpVTVdM4rBAwH/rV27EII4NJ+mNMPij4C\nvX8H5xxsVF6zlzHKZ8tRP5FjFgWH4ubsQIcapc0OReQHpapDg8EQ9LOx2SqFbrV9ebJGab5ec4wD\noREmBWhdtppO/RLoorX20lp7aq0Laa0zsyWpIXBCa31Kax0HzAG6pnPeJ8AXwG3rhSyEAIwG8789\nZewi7b/A2DGa02r2yplRP2FTcQkWlu+7SNuqpSjoKm22RA5p9X/gXsSoMZliElApxafdqlO4gAtv\nzt3D7fhEE4O0DlslcZe11tkpk+wLpCz6EpJ07A6lVF2Mna4r7vdGSqkhSqkgpVTQlStXshGKEPnQ\nrevwW09IiIF+88FLpsBE9m04Gkb4rXhpsyVylrs3tBkN57ff01mmsIcL456uxfGwaMatPmpKeNZk\nqyQuSCk1VynVJ2lqtUcGXRyyRCnlAHwFvP2gc7XWU7XW9bXW9YsXL/6wlxYi74uPgdm94cZZ6D0b\nSt6zkkGILFkcHEqxgi40ryhttkQOq90PfOvBmo/gdurVXI9VKs6AxmX5actptp64alKA1mGrJM4T\nuAW0BTon3Tpl4nWhQMoiLn5Jx5IVAqpjlCs5AzwKLJXNDUI8pMQEmP8CnN8BPX+Eck3NjkjkchG3\n4ll3OIzOtXxwcpQ23SKHOThAx3EQHQYbv7jn6ZEdqlC+uAfv/LGXiJjcW77WVrtTn0/nNigTL90J\nVFRKBSilXIDewNIU7xuhtS6mtS6ntS4HbMdYexdki89DiHxBa1j5DhxdYezsqpreMlQhsmblgYvE\nJVpkKlWYx7ce1B0A/06GsCOpnnJ3ceSbZ2pzJSqWUUsOmBTgw7NqEqeUejfpfqJSakLa24Ner7VO\nAF4DVgOHgXla64NKqTFKqS7WjFUIkWTTeNj1CzR9AxoNMTsakUcs2h1KheIe1PD1MjsUkZ+1HgUu\nHrDq3VSbHABq+nkzrHVFluy5wNK9F0wK8OFYe7tQ8maGbI+Maa1XAivTHEu3QJTWumV2ryOEAHbP\nhA2fQs3exkJgIazg/PVb7DhznRHtAvNcr0qRy3gUg8c/MmYbDi+9Z6bhlZYVWH8kjA8X7adhuSKU\n8srBckpWYNWROK31sqT7X9O7WfNaQoiHdGw1LBsOFR6Hrt8ZhTKFsIIle4ylzF1qSZstYQfqPQ8l\na8Cf/wdxt1I95eTowNfP1CY+UTNi/l4suaybg62K/VZSSk1VSv2llFqffLPFtYQQ2RCyC/4YaBTG\n7DUDHJ3NjkjkEVprFgaH0jCgSJ7qUSlyMUcnY5NDZAhs+eqepwOKefBhpypsPn6VGdvO5Hh4D8NW\n1Rf/ACYD04DcX01PiLzk2kmY9TR4FIe+f4BrIbMjEnnIvpAITl25yeDm5c0ORYi7yjaGms/A1m+h\nVh8oWiHV030blmHtoct8vuoIzSoW45ESuePnoq32fSdorX/QWu/QWu9KvtnoWkKIzIoOg5ndjY+f\nXQSFSpobj8hzFgWH4uLkQEdpsyXszRNjwNEFVv/fPU8ppfjiqZoUcHHkjbl7iEuwmBBg1tkqiVum\nlHpFKVVaKVUk+WajawkhMiM2Cn5/Cm5eMUbg0vwlKsTDik+0sGzvBdpUKYGXu0zRCztTqBS0fB+O\n/QlH/7zn6RKF3Pi8R00OhEYyYd1xEwLMOlslcc8BI4B/gF1Jt7xZy23fPPi6Ooz2Nu7TtPgQwi4k\nxsO8AXDpADw9HfzqmR2RyIO2HL/KtZtxdKstteGEnWo4FIpVgj/fh/h726+3r16Kp+v58f3fJ9h1\n9roJAWaNrYr9BqRzy3sLJPbNg2XDIOI8oI37ZcMkkRP2RWtY+jqcXA+dv4VK7cyOSORRC4NDKVzA\nmZaBJcwORYj0OblAhy/gxmnYNjHdU0Z1roqPtztvzt3LzdiEHA4wa6xd7PfxpPse6d2seS27sG6M\n0W8ypfgY47gQ9mLdf2DvbGj1AdR91uxoRB4VdTuevw5eolNNH1ycpM2WsGMVHocqXWDT/yD8/D1P\nF3Jz5qtetTl/4xafrjhkQoCZZ+3/aY8l3XdO55aZ3qm5S0RI1o4LkdP+nQpbvjbqJLUYYXY0Ig/7\n88AlYhMsdJM2WyI3aPeZcf/Xh+k+3TCgCENbVGD2jvOsPXQ5BwPLGmsX+/046T67vVNzFy+/DJ7Q\n8NdHEBudo+EIkcqhJUarmcCO0HG8FPMVNrUoOJRyRQtQt4y32aEI8WDeZaD523BoMZz6O91T3nyi\nIlVKe/L+wn1cjY7N2fgyyWZj3kqpJ5VS7yqlRiXfbHUt07QeBc7uqY85uUPZpvDPBJjU0PhFqnNX\nBWiRB5z9BxYMBr8G0PMno9ilEDZyMSKGbaeu0a2Or7TZErlHk9ehcDlY+a6x+SsNVydHvnmmNpEx\nCYxcuB9th7/LbdWxYTLwDPA6oICngbK2uJapavaCzhPAyx9Qxn2XCfD8Shj0F7gXMXYE/tbTKLAq\nRE4IOwyze0PhstB3LrhI1XxhO4uDQ2n39Sa0htk7zrE4ONTskITIHGc3aP8FXD0K/05J95TAUoV4\nt30gaw5dps6YNQS8v4KmY9fbzfe5skVmqZTap7WumeK+ILBKa93c6hfLhPr16+ugIBMqnCQmwM4f\nYf1nkBgHzd6EZm/cO3qXn+2bZ2wEiQgxpqdbjzKSY5E9EaHw0xNgSYQX1xhTBkLYyOLgUEYu3EdM\n/N3CqO7Ojnzeo4asjRO5x+9Pw9lt8HqQUUsujUW7Qnh7/l5StlW19fe5UmqX1rr+g86z1XRqcvGV\nW0opHyAeyH/lux2d4NGX4bWdUKUzbBwL3z8Kx9eYHZl9kBIt1hUTbhTzvR0J/f6QBE7Y3OerDqdK\n4ABi4hMZt/qoSREJkQ3tx0JiLKz5ON2nx685liqBA/v5PrdlxwZvYBywGzgDzLLRteyfZ2l46icY\nsAQcnI1ftHP7yy5WKdHy8FIWmx5fCcKOQO/foXRNsyMTediVqFg+WnyAy5HpL/a+EB6T7nEh7FLR\nCsb6uH1z4Nz2e57O6PvZHr7PrZ7EKaUcgHVa63Ct9QKMtXCVtdZ5b2NDVpVvCS//Y0wZHl8L3zWA\nLd9AQpzZkZlDSrQ8nLQjmYmxxuhvtP1uhxe5W3RsAl+tOcZj4zYwe8c5PFwc0z3Px1uWjIhcpvnb\n4OkLK98xlqOkkNH3sz18n1s9idNaW4BJKR7Haq0jrH2dXMvJxfhmefVfKN8K1n4MU5rDmS1mR5bz\nPDOYYXdwgoOL7vmPJJLE3IBjq2HFW/eOZCbGyUimsLq4BAvTt57msS83MGHdcVpVLsGatx7js+41\ncHdOnci5Ozsyol2gSZEKkU0uHkbtuEv7YdcvqZ4a0S7Qbr/PbVV3YJ1SqiewUNvjnlx7ULgs9Jll\nNOFdNQKmPwk1n4EnPoFCJc2OzvYiL4DFcu9xRxdwKwx/DDT62zV/G6o/lb9LZISfN4b4z20z7sMO\nAff5byUjmcJKLBbNsn0XGP/XUc5fj6Fx+aK836EytfyNWnABxTwAGLf6KBfCY/DxdmdEu0DZ1CBy\np6rdIKAFrPsEqnYHj6IAd76f7fH73Fa7U6MADyABY5ODArTW2tPqF8sE03anZlbcLdjyFWz91qgz\n9/iH0OAFcEh/qiLXu3EGfu0Ct67Doy/B3jmpd6dW7wmHl8Km8XD5gFHHp+kbULsvOLmaHb1tWSxw\n5bBR5+3cduMWmZSUuRQC/4ZQpjGUeRQWvXT3uZS8/OHNAzkbt8hzNh+/wthVRzh4IdIoeNqhMi0q\nFpM6cCJvCzsCk5tCnf5Gr2mTZHZ3qk2SOHtj90lcsqsnYOXbRvXo0rXgya/A74H/hrnL1eNGAhd/\nC55dCL71Mj5Xazj2J2waB6G7oJAPNB0OdQfkndpn8bfhwu67o2zn/oXYpNUHBUtB2cZ3k7YS1VKP\nSCaviUs5persbtQulDItIpv2h0Qw9s/DbD1xDb/C7rzTNpAutXxwcJDkTeQTf/4fbP8eBq8H37qm\nhGBqEqeUWqe1bv2gYzkl1yRxYCQuBxfB6v+DqEtQ7zlo/TEUKGJ2ZA/v0gGY2c34+NnFUKp65l6n\nNZzaYDQrPrsFPIpD41ehwYvgWsh28T6MjOrf3boO53ckJW3b4EKwsY4NoFhg6qTNu+yDW2VJnT1h\nJWeu3mT8X0dZvu8ihQs48/rjFen3aBlcnfLojIAQGbkdARPrG2WaXlgDDjZrbpUhU5I4pZQbUADY\nALTEmEYF8AT+1FpXttrFsiBXJXHJYqPg77Gw/Qdw84InxhjrxdZ/kjt/YYfsgt96GItHByyFYo9k\n733O/mNMs55cB27eRh2+RkPBvbB1430Y6Y2QKUcoWBKiLhiPHZzBp46RrJVpDP6N7qy/ECInXYmK\nZeL648z69xzOjg682DyAwS3K4+nmbHZoQphnz2xY/BJ0nWRMreYws5K44cAbgA8Qyt0kLhL4UWv9\nndUulgW5MolLdukArHgbzm8H5QA6xWaA3DJ1dmYrzOoFHsWMBK6wFTqwhe4yRuaOrjDWijV8ER59\nFQoWf/j3zq6YG8bI2ryBd6dEU3JygxYjjKTNt6507hCmio5N4MdNp/hx8yliEyz0buDP8NYVKeHp\nZnZoQpjPYoFf2sP1U/BaELh75+jlzZ5OfV1rPdHqb5xNuTqJA+ObaVwFiLl+73P2voj9xFqY098Y\nlh6wJOOyItl1+SBs/h8cWGgkSfUGQtNh4Olj3eukFXcLLu2D0N1GQnlht/Gf/b4UjA63bVxCPEBc\ngoXZO84xYd1xrt2Mo2ONUrzTNpDyxQuaHZoQ9uXiXpjyGDR6CTqMzdFLZzaJs0ndBntK4PIEBwdj\nlCc99lxO4vBymP88FA801sB5FLP+NUpWg6d+hpYjYcvXsGMqBP0EtfsZfWoLl3v4ayTGG8nihd1G\n0nYh2Ggyr5Pq2Hn6GlOjdfqDT11Y8ipEptMc2cvv4WMRIpssFs3y/RcZv/oo567folFAEaZ1qEyd\nMna0FEEIe1K6FtQfZPxeqfus8fvGzsju1Nzi6+pJlfnTcC4Aw/eZO42Ynn1/wKKhxrRhv/k5NxR9\n4yxs/QaCfzOKBdfsBc3eguKVMrcJwGKBa8eTkrWkpO3SfqMbAhhr73zqGp+Xbz0jeUvbMFl2jQqT\nLQ4OTVXTqkstHzafuMKB0EgqlyrEex0q07JScSkXIsSD3LoOE+tBiaowcPmDN5tZiZQYSSFPJHHp\nJQYOTkbS4e4FbT8z6qjZww/lXdNh2RtQrhn0mW3ODtLIC/DPdxD0MyTcPMI/UwAAIABJREFUNhKu\ny/shIUWvR2d3Y+dvoVJ3R9gu7IG4qKTnPcCntpGo+dY1krfC5TL3NZZdo8Iki4NDGblwPzHxqTue\neLs7M6pzVbrW9sVRyoUIkXlBP8PyN6HnT1DjqRy5pNlr4qTEiC2klxiUrgVLhxkbHwJaQKdvjGa+\nZtn2PaweCRXbQq8Z5i/ej75i1PvZ8jX37XLg6AIlq99N1nzrGh0j8mrBZZFnNf58HRcjbt9z3MfL\njX9GmvIjWIjczZII39YylslonSN/mJuyJi5FiZFiSqnCpC4xkqn+FEqp9sC3gCMwTWs9Ns3zLwGv\nAolANDBEa33IOp+BnavZK/1vmudXGb3e1o6GH5rAY+9Bk9fBMQdLBGgNm8fD+k+hShfjLxYnl5y7\nfkYKFoc2HyclcRkYvMFY65DXu0GIPOvUlWjWHr7M2kNh6SZwQIbHhRAPcGABRIfdrQ4Rcd6YGQPT\nZ1isvbFhKHdLjOwidYmRB5YXUUo5ApOAJ4AQYKdSammaJG2W1npy0vldgK+A9lb7DHIjBwejTVdg\nB1j1Lqz7j7Fbs8u39++IYC1aGwnk1m+gZm+jro699Tr18kt/TaGXv2kVuYXIroREC7vPhRuJ2+HL\nnLpyE4AqpT0p5OpEVGzCPa/x8ZaSNkJky7oxd9dFJ4uPMY7npSROa/0t8O1DlBhpCJzQWp8CUErN\nAboCd5I4rXVkivM9uO8cWT7j6QPP/GbsCl35DkxrAw2HGr1YXW1UPsBigT/fM3bv1B8EHf9nSnXr\nB2o9Kv3NBq1HmReTEFkQHZvA5mNXWHP4MhuOhHHjVjzOjopHyxflucblaF2lBH6FC6S7Js7d2ZER\n7QJNjF6IXCyjKhB2UB3CVsMll5RShbTWUUqpD4G6wKda690PeJ0vkHK4JARolPYkpdSrwFuAC/B4\nem+klBoCDAEoU6ZM1j+D3KxKJwhobvyV8O9kOLLc6MNaqa11r2NJhKWvw57fofFr0PZT+9hYkZ7k\nv5Zks4HIRS6Ex7Du8GXWHA5j+8lrxCVa8C7gTKvAErSpUpIWlYpRKE1nhW51jJUrKXenjmgXeOe4\nECKLMpzJMb9slK02NuzTWtdUSjUDPgXGAaO01vckZGle9xTQXmv9YtLjZ4FGWuvXMji/L9BOa/3c\n/d43z2xsyI5z/xojUFeOQLUe0OELKFji4d83MR4WDjb6vLYcaazDs9cETohcQmvNwQuRrDlkTJMe\nvGBMPJQrWoAnqpakTZWS1CtbGCdHOxztFiKvMqFslKnFfjE2HQA8CUzVWq9QSn2aideFAv4pHvsl\nHcvIHOCH7IWYT5RpBEM3wZZvjI0HJ9cbI2Z1+mc/6Yq/DX8MhGOr4IlPjA4JQoj7Slu7LXl07HZ8\nIttOXWPtocusOxzGpcjbOCioV7Yw73eoTJsqJalQ3ENquglhFjueybHVSNxyjOTrCYyp1Bhgh9a6\n1gNe5wQcA1onvX4n0FdrfTDFORW11seTPu4MfPygbDVfj8SldOUYLBsO5/6Bcs2h87dZL0cSdxNm\n94HTG+HJ/0GDF20TqxB5SHrr1JwdFZVLFeLklZvcikukgIsjLSoWp03VkrQKLE7RgrJbWoj8yuyR\nuF4YO0bHa63DlVKlgREPepHWOkEp9RqwGqPEyM9a64NKqTFAkNZ6KfCaUqoNEA/cAO47lSpSKF4J\nBq6A4Bnw1yj4vjE89i40GZa5ciC3I+D3XhCyA7pNhtp9bB+zEHnAuNVH7ym+G59oTJ32bVSG1lVK\n0rh8UdycpS6hECLzbNaxIWk9XEWt9S9KqeJAQa31aZtc7AFkJC4dUZeMciSHlhjtRLpMBL/7JP03\nr8Fv3eHyIeg5Dap1y7lYhcjlAt5fke42egWcHvtkTocjhLBzmR2Js8nqWKXUx8B7wMikQ87Ab7a4\nlsimQqWMjgq9Z0NMuFGOZOW7EBt177lRl2D6kxB2BHrPkgROiCwqWjD9kW6p3SaEeBi2mk7tDtQB\ndgNorS8opUxooCkeqHJHo8fp+k+MWm9Hlhtr3WKj7i7idHAEHODZBUZrLyFEpu06e4PImHgUqYta\nSu02IcTDstU+9ThtzNNqAKWUh42uI6zBzRM6joMX/gJXT5jdGxa9lFQXR4MlwdjJGnXJ7EiFyFUO\nhEYw8Jcd+Hi7858uVfH1dkcBvt7ufN6jhtRuE0I8FFvtTn0HqIixO/VzYBBGu6zsdHF4aLImLgsS\n4mDcIxAbce9zXv7w5oGcj0mIXOjIpUh6T92Oh4sT815qjK9MnQohMsnU3ala6/FKqScweqYGYhT6\nXWOLawkrc3KB2Mj0n7ODFiNC5AYnwqLpP+1f3JwcmT34UUnghBA2YbMu5UlJ2/+zd9/xVdX3H8df\nH0IgYcuWpYiIIKBoFBS1rgq2irbOipMqjp+rw7a2P63aWm39dWjV1jqKe1sFHFgBB4pAGLKRPWUT\nZoCMz++Pcy6EcEPWvTk3yfv5II/knnPu9/u559xwP/me7/ivmbUENiSrHkmCFF5iRCTVLd2wncFP\nfwUYL13fl04tGkQdkojUUAntE2dm/czsEzN728z6mNlMYCawxswGJrIuSaIz7wmWFClKi8WLlGpl\nTi6XPzWB3fmFvHRdX7q0ahR1SCJSgyW6Je4x4NdAU2AMcI67f2VmRwKvAB8muD5JhhReYkQkVa3Z\nspPLn/qKLTvzeOX6fnRrqwH5IpJciU7i6rr7RwBmdr+7fwXg7nO17l810/sSJW0iZbR+2y4uf+or\n1m/dxQvX9aVn+6ZRhyQitUCik7jCIj/nFtuXnKUhREQilLNjN1c8PYGVObk8d+0JHNvpoKhDEpFa\nItFJ3NFmtoVgNZnM8GfCxxkJrktEJFJbduZx5TMTWbR+O89efTx9D2sRdUgiUoskNIlzd63eLCK1\nwvZd+Vz770nMXb2FJ688jpO7tow6JBGpZZI2xYiISE2Vu7uAHz83iWnLc3j88j6ccWSbqEMSkVoo\nWctuiYjUSLvyCxj6QjYTFm/kL5cczcCeB0cdkojUUkriRETKKK+gkP95aSqfz1/PHy/szfnHaO1T\nEYmOkjgRkTLILyjkjlen8fGcNfzu/KO4JKtj1CGJSC2nJE5EpBSFhc4v3pzOezO+5X+/350rTzw0\n6pBERJTEiYgciLvzm3dm8PbUlfz87CO47pTDog5JRARQEiciUiJ3574Rs3ll4nJuOf1wbjmja9Qh\niYjsoSRORCQOd+ehD+cy7MslXHdyZ3529hFRhyQisg8lcSIicfzt4/k8+ekirux3CL/5fne0/rOI\npBolcSIixfzjk4U8Mno+l2R14L5BRymBE5GUpCRORKSIZ8ct5o8fzuX8Y9rx4A97U6eOEjgRSU1K\n4kREQi9PWMb9I2cz8Ki2/Pnio0lTAiciKUxrp4pIrfXO1JU8PGoeq3JyadYgnU078jjjyNY8+qM+\n1E3T37giktqUxIlIrfTO1JXc9fYMcvMKANi0I486Buf0bEu9ukrgRCT16X8qEak18gsKWbRuGx/O\nXM09787ck8DFFHowKlVEpDpIuZY4MxsIPAKkAU+7+0PF9v8UuA7IB9YBQ9x9aZUHKiIpK7+gkKUb\ndzB/zVbmr9nGN2u3MX/NVhat387u/MIDPndVTm4VRSkiUjkplcSZWRrwOPBdYAUwycyGu/vsIodN\nBbLcfYeZ3QT8Cbi06qMVkWQp2letXbNM7hzQjQv6tN/vuLyCQpZu2B4kamu2MX/tVhas3caiddvZ\nXbA3WetwUCZHtGnMd45oRdc2jenauhE3vjiZbzfv3K/Mds0yk/raREQSJaWSOOAEYIG7LwIws1eB\n84E9SZy7jy1y/FfAFVUaoYgkVfG+aitzcvnV29NZvTmXTi0a8s2arcwPW9YWr99OXoEDYAYdD2pA\n19aNOK1ba7q2bsQRbRrTpXVDGtTb/7+6Xw48cp96ADLT07hzQLeqeaEiIpWUaklce2B5kccrgL4H\nOP7HwAdJjUhEqtTDo+bt11dtZ14hD304DwiStU7Ng2TtzO5t9iZrrRqRWS+tzPXEWvbK0uInIpKK\nUi2JKzMzuwLIAr5Twv6hwFCATp06VWFkIlIZB+qTNvLWk8udrB3IBX3aK2kTkWor1UanrgQ6Fnnc\nIdy2DzM7C/gNMMjdd8UryN3/5e5Z7p7VqlWrpAQrIonXvGG9uNvbN8ukZ/umCUvgRESqu1RL4iYB\nXc2ss5nVAy4Dhhc9wMz6AE8SJHBrI4hRRJJkwdqtbNuZR/F1EtRXTURkfyl1O9Xd883sFmAUwRQj\nz7r7LDO7H8h29+HAw0Aj4I1wUepl7j4osqBF4ijr6ErZa9P23fz4uWwaZ9bjf07vwtOfL9b5ExE5\nAHP3qGNIuqysLM/Ozo46DKklio+uhKAl6cEf9lIiUoLd+YVc9ewEpizL4dWh/Ti200FRhyQiEhkz\nm+zuWaUdl1ItcSI1QbzRlbl5BfziremMmrWaZg3SaZKZTrPMejRrkE7TzHSaZabTtEE6zRrUo2lm\nOg3rpRG2NB9QTWjxc3d+O3wWXy3ayF8vPVoJnIhIGSmJE0mwkkZX7s4vZMHabeTk5rF5R94+k9EW\nV7eOFUn2guSuWWb4uEGwbf66rbwxaeWeclbm5HLX2zMAqlUiN+zLJbwycRk3ndaFH/TpEHU4IiLV\nhpI4kQSatjynxH3tm2Xy358GM+K4OzvzCsnJ3c3m3DxydgRfm4s+zs1jc5jwrd26k/lrt5KzI4+t\nO/NLrCM3r4CHR82rNkncp9+s43cjZ/PdHm2482wNXBARKQ8lcSIJMnXZJq56ZiIHNUxn+64CdhVZ\no7P46EozI7NeGpn1Mjm4afmWecovKGTLznyO+91/idejtbqs/blg7TZueXkKR7RpzN8uPYY6dUq/\nfSwiInul2hQjItXSlDCBa96oHiNvPYU/Xtib9s0yMYIWuEQOaqibVofmDeuVuMZnw/p1SfUBSzk7\ndnPdc5OoX7cOT1+dRcP6+ntSRKS89D+nSCXFErgWjerx6tB+HNw0s0pWArhzQLf9RsGm1TG27crn\nl29N5w8/6EXdtNT7Oy2voJCbX5rCqpydvDK0Lx0OahB1SCIi1ZKSOJFKmLx0E1c/O5GWjerxSpjA\nVZV4a3/+/OwjWLxhB4+Ons/m3DweuawPGemps8KBu3Pv8Fl8uXADf774aI47pHnUIYmIVFtK4kQq\naPLSjVz1zERaN8nglev70bZpRpXHUFKL30EN0rlvxGyGDJvEv67KolGK3K58fvxSXpqwjBu+cxgX\nHqeRqCIilZF691pEqoHsJdEncAdybf/O/OWSo5mweCODn/qKjdt3Rx0Sn89fx/0jZ3NW99b8YsCR\nUYcjIlLtKYkTKadJSzZy9bMTadMkg1eHpl4CF/PDYzvw5BXHMXf1Vi55cjzfbo5u1OrCddu4+aUp\ndG3diL9d1oc0jUQVEak0JXEi5TBxcZjANc3glaH9aNMkNRO4mLN6tOH5ISewZvNOLvrHeBat21bl\nMQQjUbOpl1aHp1Lo1q6ISHWnJE6kjCYs2sA1/57IwU0zePX61E/gYvoe1oJXhvZjZ14BF/9zPDNX\nbq6yuvMKCvmfl6ewYtMO/nnlcXRsrpGoIiKJoiROpAwmLNrAtcMmcXDYAte6miRwMT3bN+WNG08k\nIz2Ny/71FV8t2lAl9d4/YjZfLNjAH37Qi+MP1UhUEZFEUhInUoqvFm3gmn9Pol2zzCCBa1y9EriY\nw1o14s2bTqRt0wyufnYiH89ek9T6Xhi/hBe+WsrQUw/j4qyOSa1LRKQ2UhIncgDjF27g2n9PosNB\nmbxyffVN4GIObprJ6zecyJFtG3PDi5N5e8qKpNTzxYL13DtiNmce2ZpfDtRIVBGRZFASJ1KCLxeu\n59phE+lwUCYvX9+PVo3rRx1SQjRvWI+Xru9Hv8Oa89PXv+bZcYsTWv7i9du5+aUpdGnVkL9ddoxG\nooqIJImSOJE4vlywniHDJtGpeQNeGVpzEriYRvXr8uw1xzPwqLbcP3I2f/loXkLWW92cm8ePn5tE\nWh3jmauPp3FGegKiFRGReJTEiRTzxYL1XDtsEoc0b8jL1/ejZaOalcDF1K+bxmOX9+HSrI48OmYB\nvx0+i8LCiidy+QWF3PLyFJZv3ME/r9BIVBGRZNOETSJFjJu/nh8/N4nOLRvy0nV9aVFDE7iYuml1\neOjCXjRrkM6Tny0iZ0cef77kaNLTyv/33e/fm8Pn89fzpwt7c0JnjUQVEUk2JXEioc/nr+O657Jr\nTQIXY2bc9b3uNGtQjz9+OJctO/P4x+DjyKyXVuYyXpqwlGFfLuG6kztzyfEaiSoiUhV0O1UE+Oyb\ndfw4TOBevr5frUngirrptC48+MNefPbNOq58ZgKbc/PK9LwvF67nt+/O4vRurbjre92THKWIiMQo\niZNa79Nv1nHd89l0adWIV67vR/OG9aIOKTI/OqETj11+LF+vyOHSJ8ezduvOAx6/ZP12bnpxCp1b\nNuTRH2lNVBGRqqQkTmq1T+at5frnszm8VSNevq4vB9XiBC7me70O5tlrjmfZxh1c/M/xLN+4I+5x\nsZGodQyNRBURiYCSOKm1xs5by9DnJ9O1dSNeUgK3j1O6tuKl6/qSsyOPC//xJfNWb91nf35BIbe+\nMpWlG3bwjyuOo1MLjUQVEalqloi5oVJdVlaWZ2dnJ6Xsd6au5OFR81iVk0u7ZpncOaAbF/Rpr7pS\nrJ7idTVvWI+cHbvp3q4JL/64L80aKIGL55s1W7nymQnszCvk2v6H8kb2Clbl5NKgfhrbdxXw0A97\ncdkJnaIOU0SkRjGzye6eVdpxaomrhHemruSut2ewMicXB1bm5HLX2zN4Z+pK1ZVC9cSra8P23RQC\nl5/QSQncARzRpjFv3ngS6XXgbx/P33P+tu8qIK2OkZFe9hGsIiKSWJpipBIeHjWP3LyCfbbl5hXw\n2+Ez2bB9d0LrenT0NzWurqhfkzs8PnYhl/c9JKF11TQdmzegbloasO9o1YJC5+FR85LWcioiIgem\nJK4SVuXkxt2+OTef342cXSUx1MS6qvI1lXQNZV9rtsQfparzJyISnZRL4sxsIPAIkAY87e4PFdt/\nKvA3oDdwmbu/WfVRBto1y2RlnA+xg5tm8OEdpya0roF/+4xvN+//QVqd60qF19SuWWZC66mpSnqv\n6/yJiEQnpZI4M0sDHge+C6wAJpnZcHcv2iyzDLgG+HnVR7ivOwd04663Z+xzmy4zPY1fDjySppmJ\nnW7hlwOPrHF1pcJrunNAt4TWU1OV9F7X+RMRiU5KJXHACcACd18EYGavAucDe5I4d18S7iuMIsCi\nYn2BqmJ0ZU2sqya+pppK509EJPWk1BQjZnYRMNDdrwsfXwn0dfdb4hw7DBhZltupyZxiRERERCSR\nav0UI2Y21MyyzSx73bp1UYcjIiIiklCplsStBDoWedwh3FZu7v4vd89y96xWrVolJDgRERGRVJFq\nSdwkoKuZdTazesBlwPCIYxIRERFJOSmVxLl7PnALMAqYA7zu7rPM7H4zGwRgZseb2QrgYuBJM5sV\nXcQiIiIi0Ui10am4+/vA+8W23VPk50kEt1lFREREaq2UaokTERERkbJJqSlGksXM1gFL4+xqCmwu\n4Wkl7Stpe0tgfYUCTK4Dvcaoyy7v88t6fGnHVXS/rn1iyq7Ic3XtyydZ1z5Vf+fLcmxF/r8/0D5d\n++Q+P1G/86UdU959VXXdD3H30kdlunut/QL+Vd59B9ieHfXrKe9rjLrs8j6/rMeXdlxF9+vaJ6bs\nijxX1z41rn2q/s5X9tpX8LNA1z4Frn1ZjkvktU+1617bb6eOqMC+Az0nFSUz3sqWXd7nl/X40o6r\n6H5d+8SUXZHn6tqXT7LiTdXf+bIcW5H/78sbQyqobde+LMfV2GtfK26nVgUzy/YyzK4sNY+ufe2l\na1976drXTql23Wt7S1wi/SvqACQyuva1l6597aVrXzul1HVXS5yIiIhINaSWOBEREZFqSEmciIiI\nSDWkJE5ERESkGlISJyIiIlINKYmrImbW0MyyzezcqGORqmNm3c3sn2b2ppndFHU8UnXM7AIze8rM\nXjOzs6OOR6qGmR1mZs+Y2ZtRxyLJF362Pxf+rg+u6vqVxJXCzJ41s7VmNrPY9oFmNs/MFpjZr8pQ\n1C+B15MTpSRDIq69u89x9xuBS4D+yYxXEidB1/4dd78euBG4NJnxSmIk6LovcvcfJzdSSaZyvg9+\nCLwZ/q4PqvJYNcXIgZnZqcA24Hl37xluSwO+Ab4LrAAmAT8C0oAHixUxBDgaaAFkAOvdfWTVRC+V\nkYhr7+5rzWwQcBPwgru/XFXxS8Ul6tqHz/sz8JK7T6mi8KWCEnzd33T3i6oqdkmccr4Pzgc+cPdp\nZvayu19elbHWrcrKqiN3/8zMDi22+QRggbsvAjCzV4Hz3f1BYL/bpWZ2GtAQ6AHkmtn77l6YzLil\n8hJx7cNyhgPDzew9QElcNZCg33sDHiL4D14JXDWQqN95qd7K8z4gSOg6ANOI4O6mkriKaQ8sL/J4\nBdC3pIPd/TcAZnYNQUucErjqq1zXPkzgfwjUB95PamSSbOW69sCtwFlAUzM73N3/mczgJGnK+zvf\nAngA6GNmd4XJnlR/Jb0PHgUeM7PvE8Faq0riqpC7D4s6Bqla7v4J8EnEYUgE3P1Rgv/gpRZx9w0E\n/SClFnD37cC1UdWvgQ0VsxLoWORxh3Cb1Hy69rWXrn3tpOsukKLvAyVxFTMJ6Gpmnc2sHnAZMDzi\nmKRq6NrXXrr2tZOuu0CKvg+UxJXCzF4BxgPdzGyFmf3Y3fOBW4BRwBzgdXefFWWckni69rWXrn3t\npOsuUL3eB5piRERERKQaUkuciIiISDWkJE5ERESkGlISJyIiIlINKYkTERERqYaUxImIiIhUQ0ri\nRERERKohJXEiUiFm9lczu6PI41Fm9nSRx382s5+WUsaXZahniZm1jLP9NDM7qYTnDDKzX5VSbjsz\nezP8+Rgz+145n3+NmT0W/nyjmV1V2msp7TVUtJxkCGMbGXUcIlIyrZ0qIhX1BXAJ8DczqwO0BJoU\n2X8S8JMDFeDucZOwMjoN2Abslwi6+3BKmU3d3VcBF4UPjwGygPfL+vxiZVV0cfvTKPIaKlGOiNRC\naokTkYr6Ejgx/PkoYCaw1cwOMrP6QHdgCoCZ3Wlmk8xsupndFyvAzLaF3+uY2RNmNtfM/mtm75vZ\nRUXqutXMppjZDDM70swOJVhk/CdmNs3MTikaWLFWsmFm9qiZfWlmi2LlmtmhZjYzXELnfuDSsKxL\niz3/PDObYGZTzexjM2tT/ESY2b1m9vOwdW9aka8CMzskXhnxXkOsnLDMY8zsq/Cc/cfMDgq3f2Jm\nfzSziWb2TfHXHh5zsJl9FpY7M3aMmQ0Mz+PXZjY63HaCmY0PY/vSzLrFKa+hmT0b1jnVzM4v8V0h\nIlVGSZyIVEjYkpVvZp0IWt3GAxMIErssYIa77zazs4GuwAkELV7HmdmpxYr7IXAo0AO4kr3JYcx6\ndz8W+Afwc3dfAvwT+Ku7H+Pun5cS7sHAycC5wEPFXsdu4B7gtbCs14o9dxzQz937AK8CvyipEndf\nFZZxDPAU8Ja7L41XRhlew/PAL929NzAD+G2RfXXd/QTgjmLbYy4HRoVxHA1MM7NWYUwXuvvRwMXh\nsXOBU8LY7gH+EKe83wBjwjpPBx42s4YlnQcRqRq6nSoilfElQQJ3EvAXoH3482aC260AZ4dfU8PH\njQiSus+KlHMy8Ia7FwKrzWxssXreDr9PJkj4yuudsOzZ8VrSStEBeM3MDgbqAYtLe4KZ9QeuJ3hd\n5S7DzJoCzdz903DTc8AbRQ4pej4OjVPEJOBZM0sneO3TzOw04DN3Xwzg7hvDY5sCz5lZV8CB9Djl\nnQ0MirUSAhlAJ4I1JEUkImqJE5HK+IIgaetFcDv1K4JWtJPY21fNgAdjLVTufri7P1POenaF3wuo\n2B+fu4r8bOV87t+Bx9y9F3ADQQJTojBRewa4xN23VaSMMjjg+XD3z4BTgZXAsFIGS/wOGOvuPYHz\nSojNCFrwYtewk7srgROJmJI4EamMLwluUW5094KwdacZQSIXS+JGAUPMrBGAmbU3s9bFyvkCuDDs\nG9eGoMN/abYCjRPwGkorqylBMgRw9YEKCVu+3iC4DfpNGcqIW6+7bwY2FenvdiXwafHjDhDHIcAa\nd38KeBo4liDBPtXMOofHNI8T2zUlFDmKoF+ihc/tU9ZYRCR5lMSJlIGZdTKzbWaWloCyhpnZ7xMR\nVwnllznWBLyuGQSjUr8qtm0z8H9m9nt3/wh4GRhvZjOAN9k/cXkLWAHMBl4kGBCxuZS6RwA/iDew\noQLGAj1iAxuK7bsXeMPMJgPrSynnJIIE9tEigxvaHaCMSwgS3Hiv4WqCvmfTCfoS3h+nvjuAFnG2\nnwZ8bWZTgUuBRwhu784C3jazr4FY378/AQ+a2RKgpBa73xHcZp1uZrMIbg2/CIn93SgqHOgxL5Fl\nJks42OS6qOOQ2sfcPeoYRFJG+EHWhuA2VcwRYSf+RNUxDFjh7v8bZ981wHXufnLxfdXNgV5nCcc3\ncvdtZtYCmAj0d/fVyYwxamb2CfCiuz8dZ9+hBH3n0t09P8H1VqpsM7sXONzdr0hgTA50dfcFiSqz\nqhzoOookkwY2iOzvPHf/OOogSmJmae5eUPqR1c5IM2tG0PH/dzU9gRMRqSzdThUpAwvmFHMzqxs+\n/sTMfmdmX5jZVjP7yIqsKmBmb5jZajPbHM7XdVQZ6uhOMOXEieHtqZxw+zAz+4cFc6dtB043s++H\n83VtMbPlYctIuWOtwOu6ysyWmtkGM7vbgtUUzirjObzezBaY2UYzGx7eZsQCfyWYXuQwglbQ7HDf\n98xsdhjLSts7OrJoufXNLMfMehbZ1srMcs2stZm1NLOR4TEbzexauU+nAAAgAElEQVRzCyYnLl7O\nfWb29/DndDPbbmYPh48zzWynhf3IzKyfBXOq5Vgw59ppRcrZc2vNzNIsWLlivZktNrNbip7v0CEl\nnO/Y6N2c8P1QfNqV2Px0sduasWt5tZktC+v8Tbxj45Vtwdx444oc/0j43tpiZpOthFvWRd9DYTnb\ninzttKB1u+h8dDlm9q2ZPWbBHH2YWSyer8PnXWrBihEritTTPTy3OWY2y8wGFdk3zMweN7P3wvM4\nwcy6lBBvhpm9GL6HcyyYv7BNuK+5mf3bzFaZ2SYzeyfcflD4HloXbh9pZh3ilR8eP8TM5oTHjrKg\nj6JIwimJE6m4y4FrgdYErUdFE4wPCKbRaE3Qv+ul0goLR/vdCIx390bu3qxYXQ8Q9CUbB2wn6L/U\nDPg+cJOZXVDBWMt0rJn1AJ4ABhPMu9aUYEqRUpnZGcCDBH3ADgaWEsyXBsH0FacCR4RlXgJsCPc9\nA9zg7o2BnsCY4mW7+y6CKTd+VGTzJcCn7r4W+BlBf7tWBLfKf00wlUZxn7J3QMXxwOowLgj6uc1z\n941m1h54D/g90Jzg/LxlwTxsxV0PnEPQp+1YIN41KunaxOpuFr4fxsd5bjwnA92AM4F7LPjjoLiy\nlD0pjLs5QZ/GN8zsgKNq3T323m0EHEQwb+Ar4e4CghU8WhKczzOBm8PnxeI5Onz+PnP1WTBgZATw\nEcF5uhV4yfadmPgy4L6w3gUEvy/xXE3wPutI0J/wRiA33PcC0IBg8urWwF/D7XWAfwOHEEytkgs8\nFq9wCyZC/jXBVDitgM+LnAORhFISJ7K/d8K/0HNif4mX4N/u/o275wKvE3zgAeDuz7r71jDBuBc4\n2oK5vyrqXXf/wt0L3X2nu3/i7jPCx9MJPiS+U5FYy3HsRcAIdx9XZILcsnaqHQw86+5TwnNyF0GL\n46FAHkFyeiRBP9057v5t+Lw8ggEHTdx9k7tPKaH8lwk+xGMuD7fFyjgYOMTd89z9c4/fGXg80NWC\nPnmnEiSQ7S0YVfsd9o4OvQJ4393fD8//fwlaDr8Xp8xLgEfcfYW7b6LYRMOh8lybsrjP3XPd/Wvg\na4LJfsvN3V909w3unu/ufwbqEySHZfUowejb34TlTXb3r8LylgBPcuD3bFH9COYXfMjdd7v7GGAk\n+ybu/3H3iWEfv5co+TzmESRvh4cjqie7+xYLpoY5B7gxfK/lxebpC8/DW+6+w923EiSIJcV+I8GU\nOnPCWP4AHKPWOEkGJXEi+7vA3ZuFXwdq3SraZ2sHwYdM7BbaQ2a20My2AEvCY/ZbxL0clhd9YGZ9\nzWxseHtnM8EHx4HKjxtrOY9tVzQOd9/B3haz0rQjaH2LPXdb+Nz24QfyY8DjwFoz+5eZxdZgvZAg\nOVpqZp/Gu6UYGgs0CM/LoQQf4P8J9z1M0DLzkQXLbsVd2D5MorIJPpxPJUjavgT6s28SdwhwcZFE\nP4eg9evgEl530Wu3PM4x5bk2ZZGQ8ixYRmyOBV0Ccghar8r0HjazGwhaNS/3YJJlzOyI8Dbk6vD3\n4g9lLY/wPMbKCi1l35bgsr7uFwimTHk1vG36p7ClryPBVDmb4ryeBmb2pAVdCbYQ3I5uZvFH5B4C\nPFLkvbGRYJ69MrVai5SHkjiRxLscOB84i+CD79Bwe1kmmS2pZav49pcJFmjv6O5NCfrSlXcS2/L6\nlmDlASDoJ0b86S3iWUXw4RZ7bsPwuSsB3P1Rdz+OoF/cEcCd4fZJ7n4+wa2tdwhaqvYTDvR4naBl\n5kfAyLDFhLBF9GfufhgwCPipmZ1ZQpyfAmcAfQhuJ34KDCBYMizWb2s58EKRRL+Zuzd093itbPuc\nM4JEoaySOXXAAcsO+7/9gqAl8aDw1v5myvAeC5/7O+B8d99SZNc/CJb46uruTQhuOZb1PbsK6Gj7\n9mXsxN757cosbGG7z917EEwJcy5B14TlQHMLBtcU9zOCVsi+Yeyx27/x4l9O0AWg6Psj092/jHOs\nSKUoiRNJvMYEM+pvIOhfE28typKsATrEOnyXUsdGd99pZicQJI7J9iZwnpmdFMZ3L2X/EH4FuNaC\nRd3rE5yTCe6+xMyOD1vQ0gn6+u0ECs2snpkNNrOm7p4HbAEKS66ClwnmRBvM3lupmNm5Zna4mRlB\nIlJwgHI+JfhAnx3eMv4EuA5Y7O7rwmNeDM/DgLDVNSPshB+vo/vrwO0WTHDcDPhlqWdqr3VhnIeV\n4zmJKrsxkB8eV9fM7gGalHDsHmbWkeA1X+X7TnYcK3MLsM3MjgRuKrZ/zQHimUDQuvYLCwadnEaw\nusSrJRx/oBhPN7NeYSvaFoLbq4XhLfwPgCfCgQzptneN38YE/eByLBjcEm+92ph/AndZOJjJzJqa\n2cUHOF6kwpTEiSTe8wS3elYSTF771YEP38cYgglZV5vZgSaWvRm438y2EvRNi9tClUjuPougQ/mr\nBC1M24C17LukVUnP/Ri4m2BS32+BLuztw9aEYGH2TQTnbQPBLVAIVipYEt7CupEgQSupjgkESWA7\ngg/jmK7Ax2G844En3L342qwxXwKZ7G11m02QVO5Z59XdlxO0tP6aIMlZTtByGO//06cIOuNPJ1g7\n9n2C5KjUKWLC29UPAF+Et+b6lfacsipD2aOAD4FvCK7JTuLfCi7uTILBI2/a3hGqs8J9Pyf4Y2Mr\nwXl5rdhz7yVYwzXHzC4pFu9ugqTtHILJkp8gSBTnluX1FtOW4A+SLQRrv35KcIsVgvdbHkGL4VqC\nyZQB/kbwvlhP8Pv8YUmFu/t/gD8S3K7dQrAc3TkViFOkVJrsV0QqJOzwn0Nwe6zUReEFzOwc4J/u\nrk7uIlJpaokTkTIzs/PCTt4Ngf8jWGJrSbRRpS4L5pf7ngVzqLUnuA33n9KeJyJSFimVxIV9SyZa\nMHnmLDO7L84x14Qj8mLrEmq9OpGqcz5BJ/NVBLcpLythug4JGMHcZZsIbqfOIbj9LSJSaSl1OzXs\neNzQg/UT0wkmNb3d3b8qcsw1QJa73xJRmCIiIiKRS6m1U8O/6LeFD9PDr9TJMkVERERSREolcRBM\nlApMBg4HHg9HnBV3YTj0+xvgJ+FoseLlDAWGAjRs2PC4I488MolRi4iIVNycb7fQJDOd9s0yow5F\nUsDkyZPXu3u8pfz2kVK3U4sK51T6D3Cru88ssr0FsM3dd4Wzgl/q7mccqKysrCzPzs5ObsAiIiIV\nlPX7/zLgqLY88INeUYciKcDMJrt7VmnHpdTAhqLcPYdgKZ2BxbZvCNdeBHgaOK6qYxMREUmkQgdL\n9porUuOkVBJnZq1iS56ES/p8l2DSxaLHFF2fcBDBaC8REZFqy92poyxOyinV+sQdTDBjdxpBgvm6\nu480s/uBbHcfDtxmZoMIZj3fCFwTWbQiIiIJUOjJX/xYap6USuLcfTrBwtPFt99T5Oe7gLsqW1de\nXh4rVqxg586dlS1KSpCRkUGHDh1IT0+POhQRkZTm7pha4qScUiqJq0orVqygcePGHHroofrFSQJ3\nZ8OGDaxYsYLOnTtHHY6ISEpz9YmTCkipPnFVaefOnbRo0UIJXJKYGS1atFBLp4hIGTioT5yUW61N\n4gAlcEmm8ysiUjaF7uoTJ+VWq5M4ERGRVOAOdeoojZPyURIXoSVLltCzZ8+klP3JJ59w7rnnAjB8\n+HAeeuihpNQjIiKVp5Y4qYhaO7ChvN6ZupKHR81jVU4u7ZplcueAblzQp33UYZXJoEGDGDRoUNRh\niIhICRx1QZHyU0tcGbwzdSV3vT2DlTm5OLAyJ5e73p7BO1NXVrrs/Px8Bg8eTPfu3bnooovYsWMH\n999/P8cffzw9e/Zk6NChxJZGe/TRR+nRowe9e/fmsssuA2D79u0MGTKEE044gT59+vDuu+/uV8ew\nYcO45ZZbALjmmmu47bbbOOmkkzjssMN488039xz38MMPc/zxx9O7d29++9vfVvq1iYhI2QRTjEQd\nhVQ3aokD7hsxi9mrtpS4f+qyHHYXFO6zLTevgF+8OZ1XJi6L+5we7Zrw2/OOKrXuefPm8cwzz9C/\nf3+GDBnCE088wS233MI99wRT41155ZWMHDmS8847j4ceeojFixdTv359cnJyAHjggQc444wzePbZ\nZ8nJyeGEE07grLPOOmCd3377LePGjWPu3LkMGjSIiy66iI8++oj58+czceJE3J1Bgwbx2Wefceqp\np5b6GkREpHLcQV3ipLzUElcGxRO40raXR8eOHenfvz8AV1xxBePGjWPs2LH07duXXr16MWbMGGbN\nmgVA7969GTx4MC+++CJ16wb590cffcRDDz3EMcccw2mnncbOnTtZtix+YhlzwQUXUKdOHXr06MGa\nNWv2lPPRRx/Rp08fjj32WObOncv8+fMr/fpERKR0QZ84ZXFSPmqJg1JbzPo/NIaVObn7bW/fLJPX\nbjixUnUX7wNhZtx8881kZ2fTsWNH7r333j1zrb333nt89tlnjBgxggceeIAZM2bg7rz11lt069Zt\nn3JiyVk89evX3/Nz7Fatu3PXXXdxww03VOr1iIhI+QXzxEUdhVQ3aokrgzsHdCMzPW2fbZnpadw5\noFsJzyi7ZcuWMX78eABefvllTj75ZABatmzJtm3b9vRZKywsZPny5Zx++un88Y9/ZPPmzWzbto0B\nAwbw97//fU8yNnXq1ArFMWDAAJ599lm2bdsGwMqVK1m7dm1lX56IiJSBByMbog5Dqhm1xJVBbBRq\nMkanduvWjccff5whQ4bQo0cPbrrpJjZt2kTPnj1p27Ytxx9/PAAFBQVcccUVbN68GXfntttuo1mz\nZtx9993ccccd9O7dm8LCQjp37szIkSPLHcfZZ5/NnDlzOPHEoGWxUaNGvPjii7Ru3brSr1FEREoW\n+yNcLXFSXhZ789RkWVlZnp2dvc+2OXPm0L1794giqj10nkVEDqyg0Ony6/f5yVlHcPtZXaMOR1KA\nmU1296zSjtPtVBERkQipJU4qSkmciIhIhArDG2LqEiflpSROREQkQk6QxWnFBikvJXEiIiIRcrXE\nSQUpiRMREYlQ4Z4+ccripHyUxImIiEQo1hKngQ1SXkriIrRkyRJ69uxZ5uOHDRvGqlWrSj0mtti9\niIikvlhLnJbdkvJSEldW01+Hv/aEe5sF36e/XuUhlCWJS5b8/PxI6hURqelis7XqbmoKS4EcIB6t\n2FAW01+HEbdBXrh+6ublwWOA3pdUquj8/HwGDx7MlClTOOqoo3j++ef5v//7P0aMGEFubi4nnXQS\nTz75JG+99RbZ2dkMHjyYzMxMxo8fz8yZM7n99tvZvn079evXZ/To0QCsWrWKgQMHsnDhQn7wgx/w\npz/9CQhWYbj99tsZOXIkmZmZvPvuu7Rp04YlS5YwZMgQ1q9fT6tWrfj3v/9Np06duOaaa8jIyGDq\n1Kn079+fJk2asHjxYhYtWsSyZcv461//yldffcUHH3xA+/btGTFiBOnp6ZU6HyIitY0XBt81OjVF\nJTEHqCy1xAF88Cv49/dL/nr3lr0XLyYvN9he0nM++FWZqp43bx4333wzc+bMoUmTJjzxxBPccsst\nTJo0iZkzZ5Kbm8vIkSO56KKLyMrK4qWXXmLatGmkpaVx6aWX8sgjj/D111/z8ccfk5mZCcC0adN4\n7bXXmDFjBq+99hrLly8HYPv27fTr14+vv/6aU089laeeegqAW2+9lauvvprp06czePBgbrvttj3x\nrVixgi+//JK//OUvACxcuJAxY8YwfPhwrrjiCk4//XRmzJhBZmYm7733XmWvhIhIrRObYkR94lLU\n6Pvj5wCj748mniKUxJVFwa7ybS+Hjh070r9/fwCuuOIKxo0bx9ixY+nbty+9evVizJgxzJo1a7/n\nzZs3j4MPPnjP2qpNmjShbt2gYfXMM8+kadOmZGRk0KNHD5YuXQpAvXr1OPfccwE47rjjWLJkCQDj\nx4/n8ssvB+DKK69k3Lhxe+q5+OKLSUtL2/P4nHPOIT09nV69elFQUMDAgQMB6NWr157yRESk7PZM\n9httGFKSzSvKt70K6XYqwDkPHXj/X3sGzafFNe0I11au9al487mZcfPNN5OdnU3Hjh2599572blz\nZ7nKrF+//p6f09LS9vRnS09P31Nf0e0H0rBhw7hl16lTZ5/y6tSpo35zIiIVsGfZLTXFpaamHUrI\nATpUfSzFqCWuLM68B9Iz992Wnhlsr6Rly5Yxfvx4AF5++WVOPvlkAFq2bMm2bdt488039xzbuHFj\ntm7dCkC3bt349ttvmTRpEgBbt26tcBJ10kkn8eqrrwLw0ksvccopp1T49YiISPmoJS7FJTEHqCy1\nxJVFrOPi6PuD5tOmHYKLl4AOjd26dePxxx9nyJAh9OjRg5tuuolNmzbRs2dP2rZtu+d2KcA111zD\njTfeuGdgw2uvvcatt95Kbm4umZmZfPzxxxWK4e9//zvXXnstDz/88J6BDSIiUjW07FaK630JrJ0D\n4/4CWEJzgMqyWDNuTZaVleXZ2dn7bJszZw7du3ePKKLaQ+dZROTA1mzZSd8/jOaBH/RkcN9Dog5H\n4vnobvjqH/DLJVC/UdKrM7PJ7p5V2nG6nSoiIhKhvSs2qCUuZS0cA536VUkCVx5K4kRERCK0d8UG\nSUlbV8OamdDljKgj2U+tTuJqw63kKOn8ioiULvY/pVriUtTCscH3w8+MNo44am0Sl5GRwYYNG5Ro\nJIm7s2HDBjIyMqIORUQkpRVqeGpqWzgaGraCNr2ijmQ/tXZ0aocOHVixYgXr1q2LOpQaKyMjgw4d\nop9HR0SkOlBLXAoqLAz6wx1+FtRJvXavWpvEpaen07lz56jDEBGRWk594lLY6q9hxwboknq3UqEW\n304VERFJBXtGp+oTOfUsGB1873J6tHGUQG8ZERGRCO1tiVNbXMpZOBba9oJGraOOJC4lcSIiIhGK\nDa9Tl7gUs2srLP8qZW+lgpI4ERGRSMVmSdCyWylm8edQmJ+SU4vEKIkTERGJUOGeFRuijUOKWTga\n0htCx75RR1KilErizCzDzCaa2ddmNsvM7otzTH0ze83MFpjZBDM7tOojFRERSQzfM02csriUsmA0\nHHoy1K0fdSQlSqkkDtgFnOHuRwPHAAPNrF+xY34MbHL3w4G/An+s4hhFREQSJjawQS1xKWTjIti0\nOKVvpUKKJXEe2BY+TA+/ii+pcD7wXPjzm8CZpo4EIiJSTe1pidNHWepYOCb4nsKDGiDFkjgAM0sz\ns2nAWuC/7j6h2CHtgeUA7p4PbAZaxClnqJllm1m2VmUQEZFUtWeKEeVwqWPBGGjWCVp0iTqSA0q5\nJM7dC9z9GKADcIKZ9axgOf9y9yx3z2rVqlVigxQREUkwLbuVIgryYPFnQStcil+TlEviYtw9BxgL\nDCy2ayXQEcDM6gJNgQ1VG52IiEhiaNmtFLN8IuzeCl3OiDqSUqVUEmdmrcysWfhzJvBdYG6xw4YD\nV4c/XwSM8dgkOyIiItWMlt1KMQtHg6XBYd+JOpJS1Y06gGIOBp4zszSCBPN1dx9pZvcD2e4+HHgG\neMHMFgAbgcuiC1dERKRytOxWilk4BjocDxlNo46kVCmVxLn7dKBPnO33FPl5J3BxVcYlIiKSLFp2\nK4Vs3wCrpsHpv446kjJR462IiEiEtOxWClk0FvCUn1okRkmciIhIhFzLbqWOBaMh8yBod0zUkZSJ\nkjgREZEIFWrZrdTgHvSHO+w0qJMWdTRloiROREQkQq5lt1LD2tmwbXW1uZUKSuJEREQiVbhnZEOk\nYciC0cH3ajA/XIySOBERkQg5sZY4ZXGRWjgaWnWHpu2jjqTMlMSJiIhEyPf0iZPI7N4BS8fD4dXn\nViooiRMREYnU3hUblMZFZukXULALupwedSTloiROREQkQlo7NQUsHAN1M+CQ/lFHUi5K4kRERCK0\nd8UGpXGRWTAaDjkJ0jOjjqRclMSJiIhEaE9LnHK4aGxeAevnVaupRWKUxImIiERo7zxxyuIiEZta\npJoNagAlcSIiIpHS6NSILRwNjdtBqyOjjqTclMSJiIhEqHDP2qlK46pcYQEs+iSY4Lcann8lcSIi\nIhFy9YmLzsopsHMzHF59VmkoSkmciIhIhGItcUriIrBwNGBwWPWaHy5GSZyIiEikNLAhMgtGQ/tj\noUHzqCOpECVxIiIiEVJLXERyN8HK7Go5tUiMkjgREZEIuQY2RGPRp+CFwaCGakpJnIiISIS07FZE\nFo6B+k2gQ1bUkVSYkjgREZEIadmtCLgHSVznUyEtPepoKkxJnIiISIQ0xUgE1s+Hzcur5SoNRSmJ\nExERiZD6xEVgYbjUVjXuDwdVkMSZWR0za5LsekRERKoj9YmLwILR0LwLHHRo1JFUSlKSODN72cya\nmFlDYCYw28zuTEZdIiIi1Zla4qpY/i5YMq7a30qF5LXE9XD3LcAFwAdAZ+DKJNUlIiJSbRWqT1zV\nWjYe8nOr9fxwMclK4tLNLJ0giRvu7nnsHYAjIiIiob2jUyMNo/ZYMBrqpMOhJ0cdSaUlK4l7ElgC\nNAQ+M7NDgC1JqktERKTa2js6VVlclVg4Bjr1g/qNoo6k0pKSxLn7o+7e3t2/54GlQPVcXVZERCSJ\n9vaJizaOWmHralgzs9qPSo1J1sCG28OBDWZmz5jZFKBmnDEREZEE2rN2qsanJt/CscH3GjCoAZJ3\nO3VIOLDhbOAggkENDyWpLhERkWrLw15xaomrAgtHQ8NW0KZX1JEkRLKSuNhb8XvAC+4+C02BIyIi\nsp/CPSMbIg2j5issDPrDdTkD6tSMtQ6S9Somm9lHBEncKDNrDBQmqS4REZFqKzawQfPEJdnqr2HH\nhhoxtUhM3SSV+2PgGGCRu+8wsxbAtUmqS0REpNryPX3iJKkWxJbaqjnjLJOSxLl7oZl1AC4Ph0x/\n6u4jklGXiIhIdVaolriqsXAMtO0FjVpHHUnCJGt06kPA7cDs8Os2M/tDMuoSERGpzva0xCmHS55d\nW2H5hBp1KxWSdzv1e8Ax7l4IYGbPAVOBXyepPhERkWqpUJP9Jt/iz6Ewv8ZMLRKTzOEZzYr83DSJ\n9YiIiFR7yuGSaOFoSG8IHftFHUlCJasl7kFgqpmNJeireSrwqyTVJSIiUm2pT1wVWDAaOp8CdetF\nHUlCJWvZrVeAfsDbwFvAie7+WmnPM7OOZjbWzGab2Swzuz3OMaeZ2WYzmxZ+3ZP4VyAiIlI1tOxW\nkm1cBJsW15iltopKaEucmR1bbNOK8Hs7M2vn7lNKKSIf+Jm7TwnnlptsZv9199nFjvvc3c9NRMwi\nIiJR0rJbSbZwTPC9hg1qgMTfTv3zAfY5payf6u7fAt+GP281szlAe4IRriIiIjVObNkt3U1NkgVj\noFknaNEl6kgSLqFJnLsnbAY9MzsU6ANMiLP7RDP7GlgF/Dxc1qv484cCQwE6deqUqLBEREQSSlOM\nJFFBHiz+DHpdVCNPcEouHmZmjQj60t3h7luK7Z4CHOLuRwN/B96JV4a7/8vds9w9q1WrVskNWERE\npIK07FYSLZ8Iu7fWuKlFYlIuiTOzdIIE7iV3f7v4fnff4u7bwp/fB9LNrGUVhykiIpIQhVp2K3kW\njgZLg86nRh1JUqRUEmfBTIfPAHPc/S8lHNM2PA4zO4HgNWyouihFREQSZ+/oVKVxCbdwDHQ4HjJq\n5nS1SZknLs4oVYDNwFJ3zz/AU/sDVwIzzGxauO3XQCcAd/8ncBFwk5nlA7nAZR5rixYREalm9q7Y\nEHEgNc32DbBqGpxecxeLStZkv08AxwLTCVqIewKzgKZmdpO7fxTvSe4+jlJalN39MeCxxIYrIiIS\njVgrhJbdSrBFYwGvkVOLxCTrduoqoE84sOA4glGmi4DvAn9KUp0iIiLVjrurFS4ZFoyGzIOg3TFR\nR5I0yUrijig67Uc4We+R7r4oSfWJiIhUS+7qD5dw7kF/uMNOhzppUUeTNMm6nTrLzP4BvBo+vhSY\nbWb1gbwk1SkiIlLtFLprZGqirZ0N21bXyKW2ikpWS9w1wALgjvBrUbgtD0jYhMAiIiLVnaOWuIRb\nMDr4XsOTuKS0xLl7LsESXPGW4dqWjDpFRESqo0J3TRKXaAtHQ6vu0LR91JEkVVJa4sysv5n918y+\nMbNFsa9k1CUiIlKdBX3ioo6iBtm9A5aOr7GrNBSVrD5xzwA/ASYDBUmqQ0REpNpzd0xNcYmz9Aso\n2FXjb6VC8pK4ze7+QZLKFhERqTEK1RKXWAtGQ90MOOSkqCNJumQlcWPN7GHgbWBXbKO7T0lSfSIi\nItWSuyb6TaiFY4IELj0z6kiSLllJXN/we1aRbQ7U/LZNERGRcijUZL+Js3kFrJ8Hx14VdSRVIlmj\nU2vNNCLvTF3Jw6PmsSonl3bNMrlzQDcu6JOc0TA1sa6a+JpqKp0/keRRDpcgsalFasGgBkhwEmdm\nV7j7i2b203j73f0viawvau9MXcldb88gNy8Yu7EyJ5e73p4BkPAPt5pYV018TTWVzp9I8hS6U0ed\n4hJj4Who3A5aHRl1JFUi0S1xDcPvjRNcbkp6eNS8PR9qMbl5BfzmnRlMW56z3/FFm8uLjkTad3v8\n41+ZuDxuXf/7zkzmrN5SoTJLOn7YF0tKrGvemq14uFqz44T/cHd8z8/Bvj3Hue+znfDn/0xdGbee\nX/9nBhOXbNwTndneWIOfY9v3jT/+McFxL09YFreuh0fNUxJSBg+PmqvzJ5IkmiYuQQryYdEncOR5\n1Jb70wlN4tz9yfD7fYksN1WtysmNu337rgL+M3UlHstiAC96gMf9scTj3dnvAzRm2658hn2x5ADl\nFymzjPX6PoXtW9fTny8KkqXw98PYm0DFkiezMJ0q+rj4PmDH7vivacfuAj6atXqfmGOJYvEYYwli\nbEfs5+LHlnT+SrqGAgWFTvaSjXw4azUrc3bGPUbnT6TyCk4OVwcAACAASURBVN21YkMirJoKOzfD\n4bWn+31S+sSZWSvgeuDQonW4+5Bk1BeVds0yWRnnQ6x9s0y++FVi30T9HxpT4+pKhdfUKKMuO/MK\nyEivuQskl8fu/ELGL9rAhzNX89/Zq1m/bTf16tYho24dduYX7nd8u2Y1f/SXSLI5Gp2aEAtHAxYs\nel9LJGvt1HeBpsDHwHtFvmqUOwd0I7PYh39mehp3DuimulKonpLqSjNj6858Bv7tMz77Zl3C66wu\nduYVMGrWan762jSyfv9frn52IsOnraTfYS147PI+TL37uzx0Ye/9zp8BN512WDRBi9QgrtGpibFg\nNLQ/Fho0jzqSKpOsKUYauPsvk1R2yoj1BaqKEXs1sa5UeE0tG9Xn7ndnctWzEzm398HcfW4P2jTJ\nSHj9qWbrzjzGzF3LqFmrGTt3Hbl5BTTNTOfso9oy8Ki2nNy15T6tk8XPX8vG9dm4bRfvTV/NZcd3\nom5asv4eFKn5tOxWAuRugpXZcMrPo46kSpmX1AGqMoWa/R740t3fT3jhFZCVleXZ2dlRhyEpamde\nAU9+uojHP1lAvbQ6/PzsI7jyxENJq2H/q27avpv/zl7Dh7NWM27+enYXFNKqcX0GHNWGgUcdTN/D\nmpNejmTsrckr+NkbX3PdyZ3533N7JDFykZrtF29+zWffrOerX9eOaTGSYtY78MbVMGQUdOoXdTSV\nZmaT3T2rtOOS1RJ3O/BrM9sF5BHceXF3b5Kk+kQqLCM9jdvP6sr5x7Tj7ndncu+I2bw1ZSUP/KAn\nvTs0izq8SlmzZScfzVrNBzNXM2HxRgoKnfbNMrnqxEMY2LMtfTodVOFk9cLjOjB9RQ5Pj1tMrw5N\nOf8YjVIVqQi1xCXAwjFQvwm0Py7qSKpUsib7rRVTjEjNcmjLhjw/5ATem/Et94+YzfmPf8GV/Q7h\nZ2d3o2lmetThxRVvAt5jOx3EqFmr+WDmt0xZFkx106VVQ276ThcG9mzLUe2aJKwT9f+e24M5327l\nl29Np2vrxvRop7/TRMqrUMtuVY57kMR1PhXSUvP/6mRJ9GS/R7r7XDM7Nt5+rZ0qqc7MOLd3O75z\nRCv+/NE3PD9+Ce/PWM3d53Zn0NHtUuo/2ngT8P7ktWl7plk5ql0TfvbdIzinV1sOb52cv6vS0+rw\n2OA+nPf3cdzwYjYjbjmZZg3qJaUukZrK0cCGSlk/HzYvh1PirjNQoyW6Je6nwFDgz3H2ae1UqTYa\nZ6Rz76CjuOi4DvzmPzO4/dVpvJ69nN+d35PDWjWKOjx25xfy+/dm7zf/nQNNMury3m2n0LF5gyqJ\npXXjDP5xxXFc+uR4bnt1Gv++5vga159QJJnca83ctIk3/XV4/87g50//BPUaQe9Loo2pCiV6st+h\n4ffaM0mL1Gg92zfl7Zv78/KEpfxp1DwG/u1zbjytCzef1qXK55bbuH03n8xby+g5a/nsm3Vs3ZUf\n97itO/OrLIGLObbTQdx/fk/uensGf/5oHr8YWDuWvBFJBNdkvxUz/XUYcRvkhXOAbv02eAy1JpFL\n1sAGzKwn0APYM1+Duz+frPpEkiWtjnHliYcyoGdbHnhvDo+Ons/waSu5//yenHpEq6TV6+7MX7uN\nj+esYcyctUxZtolCh1aN6/P93gfz0ew1bNy+e7/nRTUB749O6MT0FTk88clCerVvyjm9Do4kDpHq\nplDLblXM6Pv3JnAxebnBdiVxFWdmvwVOI0ji3gfOAcYBSuKk2mrdOINHLuvDJVkdufudYG657/c+\nmHsSOLfcrvwCJizayJi5axk9dw3LNwb/QfVs34Rbz+jKmd1b07NdU+rUMfodtm+fOEjeZMllde+g\no5jz7VZ+/sbXHN66EV3baIyTSGkc1BJXEZtXlG97DZSslriLgKOBqe5+rZm1AV5MUl0iVar/4S35\n4I5TePLTRTw2dgGfzlvHz84+gqsqOLfc+m27GDs3uE36+fx1bN9dQEZ6HU4+vCU3fedwzjiyNW2b\n7p8kVuVkyWVVv24a/7ziOM79+ziGvjCZd2/pT5OM2jVaTKS8Cl1NceVWkAdp9aBg1/77mnao+ngi\nkqwkLtfdC80s38yaAGuBjkmqS6TK1a+bxm1ndmXQ0cHccveNmM1bU1bwwAW9OLrjgeeWc3fmrt7K\n6DlrGD13LdOW5+AObZtkcH6f9pzVvTUnHtaSzHql97m7oE/7SJO2eNo2zeCJwcdy+VNf8ZNXp/HU\nVVnU0UAHkRKpT1wFfPirIIFLqwcFRbqVpGfCmfdEF1cVS1YSl21mzYCngMnANmB8kuoSiUzxueUu\neOILruh7CN3bNebxMQv3tJDdcVZXWjauz5g5axkzdy0rc4LbpEd3aMpPzjqCM45sndD526J2Qufm\n3H1uD347fBaPjpnPHWcdEXVIIilLDXHlNOkZmPQ0nHQrtO0d9IHbvCJogTvznlrTHw6SkMRZ8Cn0\noLvnAP80sw+BJu4+PdF1iaSC4nPLDftyyT77V+bkcuebwds/Mz2NU7q25LYzD+f0I1vTunHNXaf1\nqhMPYfqKzfzt4/n0at+UM7u3iTokkZRUqJa4slv8OXzwCzj8u3DWfVAnrVYlbcUlPIlzdzez94Fe\n4eMlia5DJBXF5pZ7b8a3rNu6fz+NFg3r8cWvzqjyqUmiYmY88IOezFuzhTtenca7t/RPiTn2RFKN\n5okro42L4fWroPlhcNEzQQJXy5V9tevymWJmxyepbJGUtj5OAgfBPG+1JYGLyUgPBjqk163DDS9M\nZlsJc9uJ1GZadqsMdm2FVy8HL4QfvQoZTaOOKCUkK4nrC4w3s4VmNt3MZpiZbqdKrVDSPG1Rzd8W\ntQ4HNeCxH/Vh4bpt3PnG17h76U8SqVVcfeIOpLAQ3h4K6+bBxcOgRZeoI0oZyUriBgBdCJbZOg84\nN/wuUuPdOaAbmcVa3KKevy1qJx3ekl9/rzsfzFzNPz5dGHU4Iiml0KFOsj6Na4Kxv4d578OAP0AX\nLQhVVLLeNr9396VFv4DfJ6kukZRyQZ/2PPjDXrRvlokB7Ztl8uAPe6XcVCBV7ccnd+a8o9vx8Kh5\nfPrNuqjDEUkZ7o6pLS6+GW/C53+GY6+CvjdEHU3KSdYUI0cVfWBmacBxSapLJOWk4vxtUTMz/nhh\nL+av2cptr0xlxC0n06lF1a7xKpKKCh00lWIcK6fAu/8DnU6E7/1Zoz/iSGhLnJndZWZbgd5mtiX8\n2kow2e+7iaxLRKqfBvXq8uSVwd9zQ1/IZsduDXQQcVCCUtzW1fDqYGjYCi55AerWizqilJTQJM7d\nH3T3xsDD7t4k/Grs7i3c/a5E1iUi1dMhLRryyGXHMG/NVn711gwNdJBaL1ixIeooUkjezmAk6s7N\n8KNXoFGrqCNKWUnpE6eETUQO5LRurfn52d0Y/vUqnhm3OOpwRCLl/v/t3Xd4leX5wPHvnR0ISSCB\nQICwZYS9ZUlFxVpEKhYcWDeO2lZbsdr252yl1VZbtGJRHOAelCpiURFEQPYKIEuQkUA0QMIKZJzn\n98dzAhnnZJ6Zc3+u61znnHc8733yZtx5JjrZbwlj4KNfQ+Za+OkL0LyHvyMKaDoeRinlF3eN7MCl\n6c2Z+sk2ln+b4+9wlPIbh9EpRs5aPg02vQ0jfw/dxvo7moAXUEmciLQWkUUislVEtojIr10cIyIy\nTUR2Oeeg6+uPWJVSdSMi/G1CL9olN+TuN9efXU9WqVCjNXFOOxbAZw9Dt3Fwwf3+jiYoeHpgQ5PK\nHtUoogj4rTGmGzAY+IWIdCt3zI+BTs7HZGC6Jz+DUsp34qIjmHF9PwqLHNwxey2nC4v9HZJSPucw\nhpCvivthO7x/i20+Hfe8DvSoJk/XxK0F1jifyz/WVHWyMeagMWad8/Vx4Bug/DwNVwCzjLUCSBSR\nFp77CEopX2rfNI5nJvYmIzOPP87drAMdVMgxhPgUI6eOwJsTITIGrn4Tohr6O6Kg4dF54owx7TxV\nloi0BfoAK8vtagnsL/X+gHPbwXLnT8bW1JGWluapsJRSXnBRtxR+PaoT/1y4k16tErj+/Lb+Dkkp\nnzHGIBJQvZt8p7gQ3rsRjmXCDfMgsbW/Iwoq3prsFxFpjG3yjCnZZoxZUs1z44APgHuMMcdqc31j\nzAxgBkD//v31X3ulAtyvR3Vic2Yej360lS4t4hnQtjo9MOpm7vpMnlqwnazcfFITY5kyurNO0qx8\nzoTyslsL/gB7voQr/gVpg/wdTdDxShInIrcCvwZaARuw/du+xq6lWtW5kdgE7g1jzBwXh2QCpVP1\nVs5tSqkgFhYmPHN1b654bhk3vbKKuOhIso+d9lpyNXd9Jg/OySDf2Q8vMzefB+dkAGgip3zKEarL\nbq19FVb9Gwb/AvpM8nc0QclbNXG/BgYAK4wxPxKRLsATVZ0kIgLMBL4xxjzt5rAPgbtF5G1gEJBn\njDno5lilVBCJj4lkQv9W/PV/2zlx5lxy9bsPNpF9LJ8R5zWjsNhBYbGhsNhBkfO5oNTr0vtLXheV\nvHYYCovs6/fWHjibwJXILyzmqQXbNYlTPmUIwX783y2Dj38LHUbBxY/5O5qg5a0k7rQx5rSIICLR\nxphtItK5GucNBa4HMkRkg3Pb74E0AGPMC8B84DJgF3AKuMnz4Sul/OX1FfsqbDtT5GDqJ9uZ+sn2\nOpUdFR5GRLgQGR7GqQLXI2Ezc/OZuXQPl3RLoXUTXdtVeZ/D2Cl3QsbRvfDu9dC4LVz1MoR7rWdX\nveetr9wBEUkE5gKfichRYG9VJxljllLFQGtjh679wiNRKqUCTlYl88VNv64vkeFhREaEERkmREaE\nERFmkzL7KPs6IjzsbOIWESZl/lAO/csXLuemiwgTHp+3lcfnbaVL80Zckt6cS7qlkJ4aH1p/aJXP\nhNSyW2dO2CW1iovgmrchNtHfEQU1ryRxxpifOl8+IiKLgATgf964llKqfklNjHWZXLVMjOXHP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fJm/ZGZB8Hlz1MnT7qf05adlPa53rIU3ilFJVq8vEzGFhENfUPlr0cn9cYb6ttZvWF9dr6mIn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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65b7f24850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "    best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "    bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "\n",
    "    best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "    bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "\n",
    "    final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "    bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Answer:\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
